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Health-Related Parameters for Evaluation
Methodologies of Human Operators in Industry: A
Systematic Literature Review

Nicolas Murcia, Olivier Cardin, Abdelmoula Mohafid, Marie-Pascale Senkel

► To cite this version:

Nicolas Murcia, Olivier Cardin, Abdelmoula Mohafid, Marie-Pascale Senkel. Health-Related Parame-
ters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review.
Sustainability, 2021, 13 (23), pp.13387. 10.3390/su132313387 . hal-03468494

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# Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

Nicolas Murcia, Olivier Cardin, Abdelmoula Mohafid, Marie-Pascale Senkel

► To cite this version:

Nicolas Murcia, Olivier Cardin, Abdelmoula Mohafid, Marie-Pascale Senkel. Health-Related Parame-
ters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review.
Sustainability, 2021, 13 (23), pp.13387. 10.3390/su132313387. [hal-03468494](https://hal.science/hal-03468494v1)

HAL Id: hal-03468494

https://hal.science/hal-03468494v1

Submitted on 26 Mar 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
Review

# Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

Nicolas Murcia¹,², Olivier Cardin²,* (ID), Abdelmoula Mohafid² and Marie-Pascale Senkel³

¹ Airbus Atlantic, Airbus Group, 44550 Montoir-de-Bretagne, France; nicolas.murcia@ls2n.fr

² LS2N UMR CNRS 6004–IUT de Nantes, Nantes University, 44470 Carquefou, France; abdelmoula.mohafid@ls2n.fr

³ LEMNA EA 4272–IUT de Saint-Nazaire, Nantes University, 44600 Saint-Nazaire, France; marie-pascale.senkel@univ-nantes.fr

* Correspondence: olivier.cardin@ls2n.fr

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check for
updates

**Citation:** Murcia, N.; Cardin, O.; Mohafid, A.; Senkel, M.-P. Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review. *Sustainability* **2021**, *13*, 13387. https://doi.org/10.3390/su132313387

Academic Editor: Lucian-Ionel Cioca

Received: 5 November 2021

Accepted: 29 November 2021

Published: 3 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_4.png" /></figure>

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.

**Keywords:** ergonomics; industry; human factor; fatigue; long-term physical strain; psychosocial risk; occupational disease

## 1. Introduction

Sustainability issues, and, especially, human factors, are essential components of Industry 4.0 and, therefore, by extension, of Industry [1]. New technologies have brought a lot of opportunities in industry through automation; however, human skills, especially cognitive and decision-making skills, are key to the success of complex operations because of their cognitive and motor skills that machines cannot copy. These new technologies increase the complexity of work tasks, which make the skills of workers more valuable for the industry and more important for its performance. From a performance perspective, it is crucial to take into account the human factors in the organization and improve the working conditions to gain productivity [2,3]. This integration of human factors in the organization of industrial companies requires an important development of production processes. Recent advances in industry have allowed for improvement in the speed of production, not only with the help of the mechanization and automation of production systems, but also with developments in the organization of work, especially with Lean management. These new organizational methods have made production more flexible and
Review

# Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

Nicolas Murcia¹,², Olivier Cardin²,* (ID), Abdelmoula Mohafid² and Marie-Pascale Senkel³

¹ Airbus Atlantic, Airbus Group, 44550 Montoir-de-Bretagne, France; nicolas.murcia@ls2n.fr

² LS2N UMR CNRS 6004–IUT de Nantes, Nantes University, 44470 Carquefou, France; abdelmoula.mohafid@ls2n.fr

³ LEMNA EA 4272–IUT de Saint-Nazaire, Nantes University, 44600 Saint-Nazaire, France; marie-pascale.senkel@univ-nantes.fr

* Correspondence: olivier.cardin@ls2n.fr

<figure><img src="image_2.png" /></figure>

check for
updates

**Citation:** Murcia, N.; Cardin, O.; Mohafid, A.; Senkel, M.-P. Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review. *Sustainability* **2021**, *13*, 13387. https://doi.org/10.3390/su132313387

Academic Editor: Lucian-Ionel Cioca

Received: 5 November 2021

Accepted: 29 November 2021

Published: 3 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_3.png" /></figure>

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.

**Keywords:** ergonomics; industry; human factor; fatigue; long-term physical strain; psychosocial risk; occupational disease

## 1. Introduction

Sustainability issues, and, especially, human factors, are essential components of Industry 4.0 and, therefore, by extension, of Industry [1]. New technologies have brought a lot of opportunities in industry through automation; however, human skills, especially cognitive and decision-making skills, are key to the success of complex operations because of their cognitive and motor skills that machines cannot copy. These new technologies increase the complexity of work tasks, which make the skills of workers more valuable for the industry and more important for its performance. From a performance perspective, it is crucial to take into account the human factors in the organization and improve the working conditions to gain productivity [2,3]. This integration of human factors in the organization of industrial companies requires an important development of production processes. Recent advances in industry have allowed for improvement in the speed of production, not only with the help of the mechanization and automation of production systems, but also with developments in the organization of work, especially with Lean management. These new organizational methods have made production more flexible and
customizable. However, these technological advances have also led to an intensification of workload for the operator [4]. This intensification is characterized by an increase in the production objectives as well as an increasing cognitive load for the operator, which is caused by recent developments in technologies and human-machine interfaces; with the implementation of Industry 4.0-related technologies, leading to a more sophisticated manufacturing organization that relies on intelligent physical systems and production processes [5], workers have to develop their skills and competences in order to collaborate with the machine. The recent evolutions in industry have developed and complexified these human-machine interactions, which result in a significative increase in cognitive workload for the operator, also called cognitive strain. For example, the development of Cyber Physical Production Systems (CPPS) induces a modification of the cognitive and physical workloads for the operators implied in the production process [6] as shown in Figure 1. These modifications aim to reduce the physical workload (defined as the sum of every physical action the worker has to perform) but generally result in increasing the cognitive workload. This leads to a necessary re-evaluation of the balance between physical workload and cognitive workload [7].

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Physical and cognitive workload in a CPPS (adapted from [6]).</figcaption></figure>

Regarding the physical aspect, Musculo-Skeletal Disorders (MSDs) are lesions or alterations of muscles, nerves, tendons and joints. MSDs have always been an important problem in industry because of the pain and restrictions they cause for humans. Numerous studies in the literature have demonstrated that they are caused by biomechanical factors, especially with prolonged exposure to awkward working postures, exerted forces, material handling and repetitive actions [8]. There are many factors increasing the risk of MSDs, but biomechanical factors have been highlighted as the most important risk factors [9,10]. Work rate, operator schedule and recuperation time are defining the exposure to biomechanical factor and, so, are also directly linked with the risks of MSDs. The Workplace Environment is also non-negligible in the development of MSDs. Extreme temperature, important vibrations [11] and loud noise are amplifying the biomechanical constraints.
customizable. However, these technological advances have also led to an intensification of workload for the operator [4]. This intensification is characterized by an increase in the production objectives as well as an increasing cognitive load for the operator, which is caused by recent developments in technologies and human-machine interfaces; with the implementation of Industry 4.0-related technologies, leading to a more sophisticated manufacturing organization that relies on intelligent physical systems and production processes [5], workers have to develop their skills and competences in order to collaborate with the machine. The recent evolutions in industry have developed and complexified these human-machine interactions, which result in a significative increase in cognitive workload for the operator, also called cognitive strain. For example, the development of Cyber Physical Production Systems (CPPS) induces a modification of the cognitive and physical workloads for the operators implied in the production process [6] as shown in Figure 1. These modifications aim to reduce the physical workload (defined as the sum of every physical action the worker has to perform) but generally result in increasing the cognitive workload. This leads to a necessary re-evaluation of the balance between physical workload and cognitive workload [7].

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Physical and cognitive workload in a CPPS (adapted from [6]).</figcaption></figure>

Regarding the physical aspect, Musculo-Skeletal Disorders (MSDs) are lesions or alterations of muscles, nerves, tendons and joints. MSDs have always been an important problem in industry because of the pain and restrictions they cause for humans. Numerous studies in the literature have demonstrated that they are caused by biomechanical factors, especially with prolonged exposure to awkward working postures, exerted forces, material handling and repetitive actions [8]. There are many factors increasing the risk of MSDs, but biomechanical factors have been highlighted as the most important risk factors [9,10]. Work rate, operator schedule and recuperation time are defining the exposure to biomechanical factor and, so, are also directly linked with the risks of MSDs. The Workplace Environment is also non-negligible in the development of MSDs. Extreme temperature, important vibrations [11] and loud noise are amplifying the biomechanical constraints.
Cognitive strain is directly linked to the complexity of human mental process to realize a task. The changes in Industry 4.0 induce more advanced human-machine interactions, which can be difficult to process, thus increasing the difficulty and skills required to perform working tasks. In this context, it can be significantly more difficult for the operator to realize their tasks because of the increase in cognitive workload. It is an aggravating factor in the development of MSDs because of the stress it induces for the operator. Ergonomics can be generically defined in two classes: physical ergonomics to reduce physical workload and cognitive ergonomics to reduce cognitive workload. A recent literature review showed that cognitive ergonomics is the most prolific research domain in the application field of Industry 4.0 [7].

Psychosocial constraints are becoming an emerging trend for the scientific community, regarding well-being at work. Unlike physical strain, this mental strain is more difficult to measure because it requires an understanding about how each person reacts to a given situation. Studies about well-being at work rely on questionnaires in order to obtain a subjective appreciation of the worker [12,13].

It is widely admitted that psychosocial constraints are incriminated in the develop-
ment of MSDs [14]. Job satisfaction, stress engendered by deadlines and social relations
with the hierarchy as well as with teammates are influencing the physical and cognitive
workloads perceived by the employees. Individual sensitivity, induced by the differences
in health capital between each employee, is also key in the development of industrial
occupational disease.

For many years, solutions to reduce biomechanical risks factors were extensively studied and gave good results to diminish the physical workload for the operator on each workstation. However, it remains impossible to suppress every occupational disease risk factor at work. Several literature reviews already exist, usually focused on a specific aspect of the problem. For example, an ergonomics-oriented analysis was conducted in order to classify the different studies on specific domains, such as physical, cognitive or organizational ergonomics [7]. Safety issues for operators are also considered in [15,16]. Closely looking at those literature reviews, it appears that there is a lack of studies exhibiting the evaluation methods with respect to short- and long-term effects on the health of operators. However, long-term effects are key in the development of most occupational diseases. Thus, we took the decision to separate short and long-term effects in our study.

The main objective of this article is, therefore, to provide a systematic literature review (SLR) of how the physical, cognitive and psychosocial aggravating factors are measured and evaluated, leading to occupational disease in operators in industry. The SLR was performed in order to identify both the tools and methodologies available in the literature. Considering the topic, this analysis is deeply multi-disciplinary, exploring ergonomics, social sciences, health care and technologies of information as well as the communication scientific corpus, and, therefore, provides an actual added value to a practitioner intending to examine this issue on their own industrial system. The purpose is to answer the following research questions that guided our research.

* What are the tools and methodologies used to evaluate the health parameters consid-
ered for evaluation of the working conditions of the operators in industry?

* Which methods are proposed to mitigate the impact of the working conditions on operators and their health?

* How are these methods integrated into an industrial environment?

To answer this last question, we categorized every article according to the health
objective targeted. The first category is relative to short-term fatigue, cognitive and physical;
the second category is constituted by long-term physical strain due to professional activities;
and the third one is focused on the psychosocial risks for the operator in Industry 4.0.
These categories were selected to differentiate which part of the occupational risks the
articles study.

Section 2 introduces the methodology used to make the systematic literature review.
The results are presented according to three main health parameters categories: short-term
Cognitive strain is directly linked to the complexity of human mental process to realize a task. The changes in Industry 4.0 induce more advanced human-machine interactions, which can be difficult to process, thus increasing the difficulty and skills required to perform working tasks. In this context, it can be significantly more difficult for the operator to realize their tasks because of the increase in cognitive workload. It is an aggravating factor in the development of MSDs because of the stress it induces for the operator. Ergonomics can be generically defined in two classes: physical ergonomics to reduce physical workload and cognitive ergonomics to reduce cognitive workload. A recent literature review showed that cognitive ergonomics is the most prolific research domain in the application field of Industry 4.0 [7].

Psychosocial constraints are becoming an emerging trend for the scientific community, regarding well-being at work. Unlike physical strain, this mental strain is more difficult to measure because it requires an understanding about how each person reacts to a given situation. Studies about well-being at work rely on questionnaires in order to obtain a subjective appreciation of the worker [12,13].

It is widely admitted that psychosocial constraints are incriminated in the develop-
ment of MSDs [14]. Job satisfaction, stress engendered by deadlines and social relations
with the hierarchy as well as with teammates are influencing the physical and cognitive
workloads perceived by the employees. Individual sensitivity, induced by the differences
in health capital between each employee, is also key in the development of industrial
occupational disease.

For many years, solutions to reduce biomechanical risks factors were extensively studied and gave good results to diminish the physical workload for the operator on each workstation. However, it remains impossible to suppress every occupational disease risk factor at work. Several literature reviews already exist, usually focused on a specific aspect of the problem. For example, an ergonomics-oriented analysis was conducted in order to classify the different studies on specific domains, such as physical, cognitive or organizational ergonomics [7]. Safety issues for operators are also considered in [15,16]. Closely looking at those literature reviews, it appears that there is a lack of studies exhibiting the evaluation methods with respect to short- and long-term effects on the health of operators. However, long-term effects are key in the development of most occupational diseases. Thus, we took the decision to separate short and long-term effects in our study.

The main objective of this article is, therefore, to provide a systematic literature review (SLR) of how the physical, cognitive and psychosocial aggravating factors are measured and evaluated, leading to occupational disease in operators in industry. The SLR was performed in order to identify both the tools and methodologies available in the literature. Considering the topic, this analysis is deeply multi-disciplinary, exploring ergonomics, social sciences, health care and technologies of information as well as the communication scientific corpus, and, therefore, provides an actual added value to a practitioner intending to examine this issue on their own industrial system. The purpose is to answer the following research questions that guided our research.

* What are the tools and methodologies used to evaluate the health parameters consid-
ered for evaluation of the working conditions of the operators in industry?

* Which methods are proposed to mitigate the impact of the working conditions on operators and their health?

* How are these methods integrated into an industrial environment?

To answer this last question, we categorized every article according to the health
objective targeted. The first category is relative to short-term fatigue, cognitive and physical;
the second category is constituted by long-term physical strain due to professional activities;
and the third one is focused on the psychosocial risks for the operator in Industry 4.0.
These categories were selected to differentiate which part of the occupational risks the
articles study.

Section 2 introduces the methodology used to make the systematic literature review.
The results are presented according to three main health parameters categories: short-term
fatigue, long-term physical strain and psychosocial risks. Section 3 shows an overview of the articles studied in this literature review. This section is split into three subsections and presents the result of the SLR. The main focus is to highlight the different technologies used to consider human factors in industry and the objective of considering these human factors. The first subsection explains the different methods used to measure the short-term fatigue of an operator and how manufacturing organizations are using these data as decision variables. The second subsection presents technologies and methods used in order to reduce long-term physical strain. As for the third subsection, it provides an overview about the psychosocial risks and the impacts they have on the well-being of an employee at work: flexibility, safety, work comfort, motivation and satisfaction [17]. Conclusively, in Section 4, we propose a discussion about the specificities of each health parameter, how they are measured and what the solutions are to reduce their impacts on occupational disease risk. This discussion also details the conditions in order to integrate health-related parameters in industry organizational decisions. Future perspectives and ethical questions are, finally, proposed in the last section.

## 2. Material and Methods

In this article, qualitative research has been made through a systematic literature review, based on PRISMA guidelines. PRISMA provides a standard peer-accepted methodology that uses a guideline checklist, which was followed in this article [18]. The objective of this systematic literature review is to identify the knowledge and current existing technologies in industry used to measure health-related data as well as how to consider this data within decision systems.

### 2.1. *Data Sources and Search Strategies*

We systematically searched the Scopus electronic database. Only peer-reviewed articles written in English were declared eligible. Table 1 presents the keywords used for the research. They are structured into two classes with Industry 4.0-related terms searched together with human factor-related ones. These keywords were chosen by aggregating the keywords of other related reviews, and our own keywords were added based on past experiences. The combination of keywords meant that no pertinent results could be found before 2014. Although studies related to the field of industry exist in the literature before this date, it was considered here that the technological and organizational evolution observed and foreseen in Industry 4.0 limits the validity of these studies in this new context. Therefore, the study presented here only shows publications between the years 2014 and 2021.

<table><caption>**Table 1.** Keywords for the systematic literature review.</caption><thead><tr><th>Industry 4.0/CPPS</th><th>Human Factors and Ergonomics</th></tr></thead><tbody><tr><td>Industry 4.0</td><td>Human factor</td></tr><tr><td>Industry of the future</td><td>Ergonomics</td></tr><tr><td>Cyber-Physical Production System</td><td>Well-Being</td></tr><tr><td>Industrial Cyber-Physical System</td><td>Human-in-the-loop</td></tr><tr><td>Smart factory</td><td>Health monitoring</td></tr><tr><td>Smart operator</td><td>Workplace organization</td></tr><tr><td></td><td>Job design</td></tr><tr><td></td><td>Task design</td></tr><tr><td></td><td>Musculoskeletal disorders</td></tr><tr><td></td><td>Operator fatigue</td></tr><tr><td></td><td>Mental strain</td></tr></tbody></table>

### 2.2. Eligibility Criteria

Figure 2 details the methodology of the systematic literature review. The selection of articles to review was conducted in three steps. First, we had to exclude results that are editorial material (e.g., call for special sessions in conferences) in order to only include
fatigue, long-term physical strain and psychosocial risks. Section 3 shows an overview of the articles studied in this literature review. This section is split into three subsections and presents the result of the SLR. The main focus is to highlight the different technologies used to consider human factors in industry and the objective of considering these human factors. The first subsection explains the different methods used to measure the short-term fatigue of an operator and how manufacturing organizations are using these data as decision variables. The second subsection presents technologies and methods used in order to reduce long-term physical strain. As for the third subsection, it provides an overview about the psychosocial risks and the impacts they have on the well-being of an employee at work: flexibility, safety, work comfort, motivation and satisfaction [17]. Conclusively, in Section 4, we propose a discussion about the specificities of each health parameter, how they are measured and what the solutions are to reduce their impacts on occupational disease risk. This discussion also details the conditions in order to integrate health-related parameters in industry organizational decisions. Future perspectives and ethical questions are, finally, proposed in the last section.

## 2. Material and Methods

In this article, qualitative research has been made through a systematic literature review, based on PRISMA guidelines. PRISMA provides a standard peer-accepted methodology that uses a guideline checklist, which was followed in this article [18]. The objective of this systematic literature review is to identify the knowledge and current existing technologies in industry used to measure health-related data as well as how to consider this data within decision systems.

### 2.1. Data Sources and Search Strategies

We systematically searched the Scopus electronic database. Only peer-reviewed articles written in English were declared eligible. Table 1 presents the keywords used for the research. They are structured into two classes with Industry 4.0-related terms searched together with human factor-related ones. These keywords were chosen by aggregating the keywords of other related reviews, and our own keywords were added based on past experiences. The combination of keywords meant that no pertinent results could be found before 2014. Although studies related to the field of industry exist in the literature before this date, it was considered here that the technological and organizational evolution observed and foreseen in Industry 4.0 limits the validity of these studies in this new context. Therefore, the study presented here only shows publications between the years 2014 and 2021.

<table><caption>**Table 1.** Keywords for the systematic literature review.</caption><thead><tr><th>Industry 4.0/CPPS</th><th>Human Factors and Ergonomics</th></tr></thead><tbody><tr><td>Industry 4.0</td><td>Human factor</td></tr><tr><td>Industry of the future</td><td>Ergonomics</td></tr><tr><td>Cyber-Physical Production System</td><td>Well-Being</td></tr><tr><td>Industrial Cyber-Physical System</td><td>Human-in-the-loop</td></tr><tr><td>Smart factory</td><td>Health monitoring</td></tr><tr><td>Smart operator</td><td>Workplace organization</td></tr><tr><td></td><td>Job design</td></tr><tr><td></td><td>Task design</td></tr><tr><td></td><td>Musculoskeletal disorders</td></tr><tr><td></td><td>Operator fatigue</td></tr><tr><td></td><td>Mental strain</td></tr></tbody></table>

### 2.2. Eligibility Criteria

Figure 2 details the methodology of the systematic literature review. The selection of articles to review was conducted in three steps. First, we had to exclude results that are editorial material (e.g., call for special sessions in conferences) in order to only include
peer-reviewed journal articles and conference articles. Next, we filtered articles based on their abstracts. Rayyan QCRI (http://rayyan.qcri.org, accessed on 27 November 2021) has been used in order to simplify this screening work, by tagging the articles. For example, a lot of results were addressing issues related to machine health-management and prognosis.

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Report of the systematic literature review, derived from PRISMA model.</figcaption></figure>

The last step was to examine the remaining articles with full-text screening, in order to determine their inclusion in our study. We rejected articles that were not in relation with health parameters or with industry and articles that were only focused on safety at work.

## 2.3. *Details of Included Studies*

The search based on the inclusion criteria, which are the keywords in Table 1, found a total of 490 articles between 2014 and 2021. After the application of the exclusion criteria presented before, we narrowed this number to a total of 87 articles relevant to the present systematic review of the literature. This review is constituted by 28 conference proceedings associated with 21 conferences (Figure 3) and 59 journal articles that come from 32 different journals (Figure 4), mostly targeting an industrial audience.
peer-reviewed journal articles and conference articles. Next, we filtered articles based on their abstracts. Rayyan QCRI (http://rayyan.qcri.org, accessed on 27 November 2021) has been used in order to simplify this screening work, by tagging the articles. For example, a lot of results were addressing issues related to machine health-management and prognosis.

<figure><img src="image_5.png" /><figcaption>**Figure 2.** Report of the systematic literature review, derived from PRISMA model.</figcaption></figure>

The last step was to examine the remaining articles with full-text screening, in order to determine their inclusion in our study. We rejected articles that were not in relation with health parameters or with industry and articles that were only focused on safety at work.

## 2.3. *Details of Included Studies*

The search based on the inclusion criteria, which are the keywords in Table 1, found a total of 490 articles between 2014 and 2021. After the application of the exclusion criteria presented before, we narrowed this number to a total of 87 articles relevant to the present systematic review of the literature. This review is constituted by 28 conference proceedings associated with 21 conferences (Figure 3) and 59 journal articles that come from 32 different journals (Figure 4), mostly targeting an industrial audience.
<figure><img src="image_7.png" /><figcaption>Figure 3. Conference proceedings included in the SLR.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 4. Journal articles included in the SLR.</figcaption></figure>

The literature review was conducted at the beginning of 2021; we found a relevant increase in the number of articles published over the years, with the majority published in 2019 and 2020, as depicted in Figure 5. After gathering the articles incorporated in the SLR, we classified them according to the health parameters they were focusing on.
<figure><img src="image_6.png" /><figcaption>Figure 3. Conference proceedings included in the SLR.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 4. Journal articles included in the SLR.</figcaption></figure>

The literature review was conducted at the beginning of 2021; we found a relevant increase in the number of articles published over the years, with the majority published in 2019 and 2020, as depicted in Figure 5. After gathering the articles incorporated in the SLR, we classified them according to the health parameters they were focusing on.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Frequency of publication per year.</figcaption></figure>

### 3. Results

In this section we present the results of the systematic literature review, structured around the three main impacts on the condition of operators: short-term fatigue, long-term physical strain and psychosocial risks. We notice that some articles may belong to different categories. The distribution of the articles is shown in Table 2, exhibiting a relative balance in the amount of works between the categories.

<table><caption>**Table 2.** Classification of selected articles based on their health objectives.</caption><tbody><tr><td rowspan="3">Short-term Fatigue</td><td>Short-term fatigue measurement</td><td>[16,19–33]</td></tr><tr><td>Reduction of fatigue</td><td>[7,15,24,34–43]</td></tr><tr><td>Integration of human short-term fatigue in an industrial decision system</td><td>[2,44–55]</td></tr><tr><td rowspan="3">Long-term physical strain</td><td>Long-term physical strain risks measurement</td><td>[11,16,23,28,56–73]</td></tr><tr><td>Reduction of long-term physical risks</td><td>[7,15,27,34,37,38,74–83]</td></tr><tr><td>Integration of long-term physical risks in decision system</td><td>[2,45–47,50,84–89]</td></tr><tr><td rowspan="2">Psychosocial constraints</td><td>Psychosocial parameters measurement</td><td>[16,28,37,39,90–96]</td></tr><tr><td>Integration of psychosocial risks in decision system</td><td>[2,13,37,45–47,50,53,97–104]</td></tr></tbody></table>

This section introduces a specific analysis of each class of health objective.

#### 3.1. Short-Term Fatigue

The term “fatigue” was extensively studied in the literature. In this work, we consider the definition given by [105], stating that “Fatigue is a suboptimal psychophysiological condition caused by exertion”. This definition covers all the aspects of fatigue. Considering the short-term consequences of fatigue in an industrial context, we define short-term fatigue as the loss of performance due to the efforts required for the worker to execute all their daily actions to fulfill industrial operations. This fatigue is often physical, with the energy expenditure the operator is developing in order to realize their work tasks, and it often increases with the cognitive load the operator has to deal with. The fatigue of workers has gradually become an important concern for the Industry, to improve their
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Frequency of publication per year.</figcaption></figure>

### 3. Results

In this section we present the results of the systematic literature review, structured around the three main impacts on the condition of operators: short-term fatigue, long-term physical strain and psychosocial risks. We notice that some articles may belong to different categories. The distribution of the articles is shown in Table 2, exhibiting a relative balance in the amount of works between the categories.

<table><caption>**Table 2.** Classification of selected articles based on their health objectives.</caption><tbody><tr><td rowspan="3">Short-term Fatigue</td><td>Short-term fatigue measurement</td><td>[16,19–33]</td></tr><tr><td>Reduction of fatigue</td><td>[7,15,24,34–43]</td></tr><tr><td>Integration of human short-term fatigue in an industrial decision system</td><td>[2,44–55]</td></tr><tr><td rowspan="3">Long-term physical strain</td><td>Long-term physical strain risks measurement</td><td>[11,16,23,28,56–73]</td></tr><tr><td>Reduction of long-term physical risks</td><td>[7,15,27,34,37,38,74–83]</td></tr><tr><td>Integration of long-term physical risks in decision system</td><td>[2,45–47,50,84–89]</td></tr><tr><td rowspan="2">Psychosocial constraints</td><td>Psychosocial parameters measurement</td><td>[16,28,37,39,90–96]</td></tr><tr><td>Integration of psychosocial risks in decision system</td><td>[2,13,37,45–47,50,53,97–104]</td></tr></tbody></table>

This section introduces a specific analysis of each class of health objective.

#### 3.1. Short-Term Fatigue

The term “fatigue” was extensively studied in the literature. In this work, we consider the definition given by [105], stating that “Fatigue is a suboptimal psychophysiological condition caused by exertion”. This definition covers all the aspects of fatigue. Considering the short-term consequences of fatigue in an industrial context, we define short-term fatigue as the loss of performance due to the efforts required for the worker to execute all their daily actions to fulfill industrial operations. This fatigue is often physical, with the energy expenditure the operator is developing in order to realize their work tasks, and it often increases with the cognitive load the operator has to deal with. The fatigue of workers has gradually become an important concern for the Industry, to improve their
performance and because of the growing importance of the societal impact of human
factors for manufacturing companies [7]. This first paragraph is providing an overview
about how this fatigue can be measured, what the solutions are to reduce the impact of
fatigue for the operator and how fatigue is taken into account in the organization.

3.1.1. Short-Term Fatigue Measurement

The first question that appeared is the measurement of the fatigue. In order to
take decisions based on short-term fatigue, it is required to correctly, and with precision,
understand the accumulation of the physical workload and cognitive strain for the worker.
There is also a key interest to understand what the impact is of this fatigue on the human
body and how it impacts the performance and well-being of the worker.

Wearable sensors are examples of physical methods used in order to measure the
efforts that the operator has to deploy in order to realize a task. Pistolesi et al. [19]
has shown that with the help of barometers, accelerometers and magnetometers it is
possible to obtain key information about the risk of back injury for workers that lift loads.
This approach of using wearable sensors is helping health specialists reinforce subjective
observational methods for ergonomics, which are checklists evaluating the difficulty to
realize a task and the possible strenuous situations. It has determined a key approach,
leading to a revision of International Standards for human ergonomics [20] because of the
enhancement of the accuracy of the measures. The use of these new methods is necessary
because of the evolution of the workplace, especially with new technologies being used
to enhance the human body. Another fully wearable solution has been developed by
Conforti et al. [21], in order to determine the precise force exerted on the lower limbs
up to the L5/S1 joint on the body, during a manual handling task. Heartbeat is also
an indicator used in the understanding of the difficulty of a task and on the fatigue the
operator may experience [26,29]. A more practical solution is to use a smartphone- or
smartwatch-integrated accelerometer, which monitors the movement of the body in order
to obtain information about energy expenditure during an effort [22,32]. These different
studies show that physical sensors are a good solution in order to grasp how the human
body responds when doing industrial tasks as well as identifying risks for the operator,
knowing that computer simulation is also used in some cases [23]. Physical workload is
the main cause of fatigue for the human, however with the growing of new technologies in
industry, there is an intensification of cognitive fatigue for operators. This cognitive load
has mostly developed due to the complexification of work tasks and the human-machine
interface [25]. Unlike physical strain, this cognitive fatigue cannot be directly measured.
However, this cognitive load has a direct impact on the perception and concentration of
the operator of the workload [31]. This cognitive aspect of work life also increases the risk
of work accidents and may decrease the performance of the operator [28].

3.1.2. Reduction of Fatigue

Competitivity in the manufacturing industry has induced many evolutions over the
last twenty years. Productivity, the tracking of operations and products and the security of
data have been major objectives for the workplace; however, more recently work safety and
human factors have become crucial subjects in order to improve the image of companies
and also to improve the performance of workers by ameliorating the quality of life at work
and worker well-being, by reducing the energy expenditure for operators. Technological
solutions have been developed over the years, which are contributing to the objective of
sustainability in the Industry 4.0 paradigm [35].

Increasing cognitive workload has been observed with the development of intelligent
manufacturing systems [7]. As a logical response, an important part of ergonomics in
the beginning of the Industry 4.0 era is directed at the development of practical human-
machine collaboration. Gualteri et al. [15] proposed a systematic literature review of
industrial collaborative robotics, which highlighted two principal research axis: safety and
ergonomics. Ergonomics in collaborative robotics is then decomposed in two sub-clusters,
performance and because of the growing importance of the societal impact of human
factors for manufacturing companies [7]. This first paragraph is providing an overview
about how this fatigue can be measured, what the solutions are to reduce the impact of
fatigue for the operator and how fatigue is taken into account in the organization.

3.1.1. Short-Term Fatigue Measurement

The first question that appeared is the measurement of the fatigue. In order to
take decisions based on short-term fatigue, it is required to correctly, and with precision,
understand the accumulation of the physical workload and cognitive strain for the worker.
There is also a key interest to understand what the impact is of this fatigue on the human
body and how it impacts the performance and well-being of the worker.

Wearable sensors are examples of physical methods used in order to measure the
efforts that the operator has to deploy in order to realize a task. Pistolesi et al. [19]
has shown that with the help of barometers, accelerometers and magnetometers it is
possible to obtain key information about the risk of back injury for workers that lift loads.
This approach of using wearable sensors is helping health specialists reinforce subjective
observational methods for ergonomics, which are checklists evaluating the difficulty to
realize a task and the possible strenuous situations. It has determined a key approach,
leading to a revision of International Standards for human ergonomics [20] because of the
enhancement of the accuracy of the measures. The use of these new methods is necessary
because of the evolution of the workplace, especially with new technologies being used
to enhance the human body. Another fully wearable solution has been developed by
Conforti et al. [21], in order to determine the precise force exerted on the lower limbs
up to the L5/S1 joint on the body, during a manual handling task. Heartbeat is also
an indicator used in the understanding of the difficulty of a task and on the fatigue the
operator may experience [26,29]. A more practical solution is to use a smartphone- or
smartwatch-integrated accelerometer, which monitors the movement of the body in order
to obtain information about energy expenditure during an effort [22,32]. These different
studies show that physical sensors are a good solution in order to grasp how the human
body responds when doing industrial tasks as well as identifying risks for the operator,
knowing that computer simulation is also used in some cases [23]. Physical workload is
the main cause of fatigue for the human, however with the growing of new technologies in
industry, there is an intensification of cognitive fatigue for operators. This cognitive load
has mostly developed due to the complexification of work tasks and the human-machine
interface [25]. Unlike physical strain, this cognitive fatigue cannot be directly measured.
However, this cognitive load has a direct impact on the perception and concentration of
the operator of the workload [31]. This cognitive aspect of work life also increases the risk
of work accidents and may decrease the performance of the operator [28].

3.1.2. Reduction of Fatigue

Competitivity in the manufacturing industry has induced many evolutions over the
last twenty years. Productivity, the tracking of operations and products and the security of
data have been major objectives for the workplace; however, more recently work safety and
human factors have become crucial subjects in order to improve the image of companies
and also to improve the performance of workers by ameliorating the quality of life at work
and worker well-being, by reducing the energy expenditure for operators. Technological
solutions have been developed over the years, which are contributing to the objective of
sustainability in the Industry 4.0 paradigm [35].

Increasing cognitive workload has been observed with the development of intelligent
manufacturing systems [7]. As a logical response, an important part of ergonomics in
the beginning of the Industry 4.0 era is directed at the development of practical human-
machine collaboration. Gualteri et al. [15] proposed a systematic literature review of
industrial collaborative robotics, which highlighted two principal research axis: safety and
ergonomics. Ergonomics in collaborative robotics is then decomposed in two sub-clusters,
physical ergonomics and cognitive/organizational ergonomics, the same way as in CPPS (Figure 1). In particular, Antao et al. [39] take into consideration human fatigue in the human-robot collaboration to improve the quality of this collaboration. Fatigue can also be an objective to minimize in mathematical optimization. For example, Weckenbourg and Spengler [36] used an assembly line-optimization algorithm, with the addition of collaborative robots under ergonomics constraints. Battini et al. [24] have made a list of the different assembly line and sequencing mathematical problems under the constraints of human factors. Biomechanical factors are the principal factors of short-term fatigue; another solution developed to reduce this strain was to conceive exoskeleton systems. The objective of exoskeletons is to relieve the muscular activity of the worker and to assist them during their working operations. In particular, Grazi et al. [34] have developed a passive upper-limb-support exoskeleton in order to reduce the muscular activity of the user and their heart rate.

### 3.1.3. Integration of Human Short-Term Fatigue into an Industrial Decision System

Safety and human factors have gained importance through the years as decision variables in industry, in order to achieve sustainability. The development of new technologies and CPPS have enabled the important potential of the automation of tasks; however, human-in-the-loop tasks are still indispensable because some tasks cannot be automated [48,52]. Within the CPPS paradigm, human operators require more technological performance in order to perform manufacturing tasks [51]. With a conceptual model, Suarez-Fernandez et al. [45] aim to identify the degree of incorporation of human factors into the different areas of digital and technological transformation of Industry 4.0. Paredes-Astudillo et al. [44] presented a human fatigue-aware cyber-physical Production system. This system aims to dynamically change the schedule of operations to the worker, according to their measured cognitive fatigue level. Stern and Becker [47] have shown with their experiments that the design of the human-machine interface has a significative impact on performance and work perception in cyber-physical production systems. This collaborative human-machine interface needs to be optimized in order to reduce the difficulty of cognitive tasks. An important aspect of this human-machine cooperation is the adaptability of the CPPS with the data fed back to the system. Reis et al. [53] focus on a CPPS, which enables self-adaptation in industrial equipment in order to support the human operator under a high level of stress and fatigue.

The research around short-term fatigue in industry is constituted by three different subjects: fatigue assessment, methods to reduce fatigue at work and how to integrate operator fatigue into the decision system. Short-term fatigue is a great indicator of the performance and perception of the workload for the operator, but most occupational diseases at work are due to long-term physical strain. In the next subsection, we will analyze how long-term physical strain is measured.

## 3.2. *Long-Term Physical Strain*

Also based on the definition of fatigue provided by [105], we define long-term physical strain as the loss of performance due to the accumulation of efforts and constraints applied to the worker on a daily basis. Long-term effects of physical strain in industrial occupational work are an important problem for society. Specifically, MSDs cause pain and incapacitate the worker from doing habitual activities. These physical diseases are created because of important workloads, repetition of physically difficult tasks or strenuous postures at work.

Long-term physical strain, unlike fatigue, is considered impossible to numerically evaluate because the risk of developing occupational diseases depends on hundreds of parameters over many years.

### 3.2.1. Long-Term Physical Strain Risks Measurement

Historically, physical ergonomics focuses on the prevention of injuries and musculoskeletal disorders at the workplace. The increased incidence of MSDs in industry led
physical ergonomics and cognitive/organizational ergonomics, the same way as in CPPS (Figure 1). In particular, Antao et al. [39] take into consideration human fatigue in the human-robot collaboration to improve the quality of this collaboration. Fatigue can also be an objective to minimize in mathematical optimization. For example, Weckenbourg and Spengler [36] used an assembly line-optimization algorithm, with the addition of collaborative robots under ergonomics constraints. Battini et al. [24] have made a list of the different assembly line and sequencing mathematical problems under the constraints of human factors. Biomechanical factors are the principal factors of short-term fatigue; another solution developed to reduce this strain was to conceive exoskeleton systems. The objective of exoskeletons is to relieve the muscular activity of the worker and to assist them during their working operations. In particular, Grazi et al. [34] have developed a passive upper-limb-support exoskeleton in order to reduce the muscular activity of the user and their heart rate.

### 3.1.3. Integration of Human Short-Term Fatigue into an Industrial Decision System

Safety and human factors have gained importance through the years as decision variables in industry, in order to achieve sustainability. The development of new technologies and CPPS have enabled the important potential of the automation of tasks; however, human-in-the-loop tasks are still indispensable because some tasks cannot be automated [48,52]. Within the CPPS paradigm, human operators require more technological performance in order to perform manufacturing tasks [51]. With a conceptual model, Suarez-Fernandez et al. [45] aim to identify the degree of incorporation of human factors into the different areas of digital and technological transformation of Industry 4.0. Paredes-Astudillo et al. [44] presented a human fatigue-aware cyber-physical Production system. This system aims to dynamically change the schedule of operations to the worker, according to their measured cognitive fatigue level. Stern and Becker [47] have shown with their experiments that the design of the human-machine interface has a significative impact on performance and work perception in cyber-physical production systems. This collaborative human-machine interface needs to be optimized in order to reduce the difficulty of cognitive tasks. An important aspect of this human-machine cooperation is the adaptability of the CPPS with the data fed back to the system. Reis et al. [53] focus on a CPPS, which enables self-adaptation in industrial equipment in order to support the human operator under a high level of stress and fatigue.

The research around short-term fatigue in industry is constituted by three different subjects: fatigue assessment, methods to reduce fatigue at work and how to integrate operator fatigue into the decision system. Short-term fatigue is a great indicator of the performance and perception of the workload for the operator, but most occupational diseases at work are due to long-term physical strain. In the next subsection, we will analyze how long-term physical strain is measured.

## 3.2. *Long-Term Physical Strain*

Also based on the definition of fatigue provided by [105], we define long-term physical strain as the loss of performance due to the accumulation of efforts and constraints applied to the worker on a daily basis. Long-term effects of physical strain in industrial occupational work are an important problem for society. Specifically, MSDs cause pain and incapacitate the worker from doing habitual activities. These physical diseases are created because of important workloads, repetition of physically difficult tasks or strenuous postures at work.

Long-term physical strain, unlike fatigue, is considered impossible to numerically evaluate because the risk of developing occupational diseases depends on hundreds of parameters over many years.

### 3.2.1. Long-Term Physical Strain Risks Measurement

Historically, physical ergonomics focuses on the prevention of injuries and musculoskeletal disorders at the workplace. The increased incidence of MSDs in industry led
to the development of numerous physical risk assessment methods. These risk assess-
ment methods can be divided into three categories, namely observation-based measures,
self-evaluation questionnaires or measurement methods [9]. Risk assessment with mea-
surement methods can be physical, with the usage of sensors and tools to monitor variables
relative to work exposure. The objective of physical ergonomics is also to reduce risks for
the worker by creating a safer workplace through design improvements of the workstation
or creating technical solutions to reduce physical strain for the worker. Physical measures
are not the only technical tools used in order to obtain precise information on the human
body during industrial tasks. In the Industry 4.0 context, a lot of research has started
focusing on the virtual environment, in order to obtain information about the realization
of industrial tasks. Motion capture, in particular, is trending in industrial applications
and is a technology that can solve problems that require precise sensor solutions. This
technology mostly consists of inertial measurement units and visual cameras, which can
be used to support solutions relating to human factors and for human safety [56]. An
application of the motion capture technology is the simulation of manufacturing task, with
a mixed or augmented reality system, in order to assess the parameters of the ergonomics
of a workstation [59,62,66,68,70]. The objective of this simulation, and the creation of a
human digital twin [57,64], is to make a virtual ergonomic assessment of the occupational
physical constraints. The main contributions of the mixed reality system used to assess
the ergonomics risks on the operator are the possibility to compute precise data about the
realization of the work task and to avoid the subjectivity of the ergonomic assessment
with the help of a health specialist. For instance, Caporaso et al. [71] focus on the human
shoulder joint in industrial tasks and analyze the biomechanical effort of the shoulder
during physical activity.

3.2.2. Reduction of Long-Term Physical Risks

In order to reduce the long-term physical strain, the principal idea offered by er-
gonomics is to reduce the constraints on the human body by adapting the workstation
design to match the regulations defined by health specialists. A user-centered approach
for workplace design can be used in order to reduce long-term physical strain for the
operator [77]. The process of optimizing workplace design, before the construction of the
workstation, is possible because of the virtual ergonomic assessment; it becomes possible
to predict the work process and to verify human operator posture beforehand. Within the
simulation of the digital twin of a human operator doing manufacturing tasks, it is also
possible to realize an ergonomic assessment of the constraints, following ergonomic obser-
vation methods [7]. Papetti et al. [75] indicate that human-centered design for improving
the workplace has shown that efficient ergonomic assessment needs multiple perspective
measurement methods, and that ergonomics risks are constituted of a combination of
physiological parameters measurement, expert based methods and self-report techniques.

Long-term physical risks are aggravated with repetitive movements. In order to break these repetitive tasks, an idea developed by operation research is to create job rotation between workers. The most popular objective of the job-rotation problems in operation research is to balance the workload, measured by ergonomic assessment methods for the worker [106]. These optimization ideas were developed in order to help work organization integrate long-term physical risks into decision systems.

The learning process of a task has a direct impact on the productivity of the worker because it affects the actions required to perform a task, thus being key for performance in industry [82,83]. This learning process is important for the worker because a well-executed movement reduces physical strain during task execution.

3.2.3. Integration of Long-Term Physical Risks in Decision System

The next challenge is to take into consideration these ergonomics measures and
solutions into the decision, aiming to reduce long-term physical risks for human operators.
Gualtieri et al. [84] present an evaluation of a robot-human collaboration in an assembly
to the development of numerous physical risk assessment methods. These risk assess-
ment methods can be divided into three categories, namely observation-based measures,
self-evaluation questionnaires or measurement methods [9]. Risk assessment with mea-
surement methods can be physical, with the usage of sensors and tools to monitor variables
relative to work exposure. The objective of physical ergonomics is also to reduce risks for
the worker by creating a safer workplace through design improvements of the workstation
or creating technical solutions to reduce physical strain for the worker. Physical measures
are not the only technical tools used in order to obtain precise information on the human
body during industrial tasks. In the Industry 4.0 context, a lot of research has started
focusing on the virtual environment, in order to obtain information about the realization
of industrial tasks. Motion capture, in particular, is trending in industrial applications
and is a technology that can solve problems that require precise sensor solutions. This
technology mostly consists of inertial measurement units and visual cameras, which can
be used to support solutions relating to human factors and for human safety [56]. An
application of the motion capture technology is the simulation of manufacturing task, with
a mixed or augmented reality system, in order to assess the parameters of the ergonomics
of a workstation [59,62,66,68,70]. The objective of this simulation, and the creation of a
human digital twin [57,64], is to make a virtual ergonomic assessment of the occupational
physical constraints. The main contributions of the mixed reality system used to assess
the ergonomics risks on the operator are the possibility to compute precise data about the
realization of the work task and to avoid the subjectivity of the ergonomic assessment
with the help of a health specialist. For instance, Caporaso et al. [71] focus on the human
shoulder joint in industrial tasks and analyze the biomechanical effort of the shoulder
during physical activity.

3.2.2. Reduction of Long-Term Physical Risks

In order to reduce the long-term physical strain, the principal idea offered by er-
gonomics is to reduce the constraints on the human body by adapting the workstation
design to match the regulations defined by health specialists. A user-centered approach
for workplace design can be used in order to reduce long-term physical strain for the
operator [77]. The process of optimizing workplace design, before the construction of the
workstation, is possible because of the virtual ergonomic assessment; it becomes possible
to predict the work process and to verify human operator posture beforehand. Within the
simulation of the digital twin of a human operator doing manufacturing tasks, it is also
possible to realize an ergonomic assessment of the constraints, following ergonomic obser-
vation methods [7]. Papetti et al. [75] indicate that human-centered design for improving
the workplace has shown that efficient ergonomic assessment needs multiple perspective
measurement methods, and that ergonomics risks are constituted of a combination of
physiological parameters measurement, expert based methods and self-report techniques.

Long-term physical risks are aggravated with repetitive movements. In order to break these repetitive tasks, an idea developed by operation research is to create job rotation between workers. The most popular objective of the job-rotation problems in operation research is to balance the workload, measured by ergonomic assessment methods for the worker [106]. These optimization ideas were developed in order to help work organization integrate long-term physical risks into decision systems.

The learning process of a task has a direct impact on the productivity of the worker because it affects the actions required to perform a task, thus being key for performance in industry [82,83]. This learning process is important for the worker because a well-executed movement reduces physical strain during task execution.

3.2.3. Integration of Long-Term Physical Risks in Decision System

The next challenge is to take into consideration these ergonomics measures and
solutions into the decision, aiming to reduce long-term physical risks for human operators.
Gualtieri et al. [84] present an evaluation of a robot-human collaboration in an assembly
line with ergonomics considerations. For this study, an algorithm was developed to analyze the performance of the collaboration in terms of ergonomics, safety, production efficiency and economics. Perruzini et al. [85] proposed an exploration of the different tools used to assess ergonomics and performance at work as well as feedback on the feasibility of the operator 4.0 interface. However, these tools need to be adaptative in order to respond to the cognitive needs of the worker, for example, to assist the ageing workforce with new machine interfaces [89]. The emotional response of workers, the applicability of the method and the needs of large empirical data are the limitations highlighted in the study. To simplify the integration of human error in the decision system, Angeloupou et al. [86] computed a simulation model using performance indicator such as: Experience, Stress, Safety or cognitive complexity of a task. These new management trends in Industry 4.0 are aiming to strengthen the human-machine collaboration; however, this collaboration bring possible occupational risks for the human [87].

### 3.3. *Psychosocial Risks*

Psychosocial constraints at work are an important risk factor in the development of occupational diseases at work [12]. The idea of this paragraph is to list the different methods to evaluate the different psychosocial parameters and their impacts on the well-being and physical risks of the worker. The second is to understand how psychosocial constraints are integrated into industrial organizational and decisional systems.

#### 3.3.1. Psychosocial Parameters Measurement

There are six identified factors in the development of psychosocial risks [4]: intensity and quantity of work, emotional strain, lack of independence, social relations, value conflicts and insecure work situation. These psychosocial risks factors are impacting the perception of the workload for the human and are often associated with stress and possible burnouts. Psychosocial parameters are harder to measure than fatigue and ergonomics assessments because they rely on a questionnaire answered by the worker and are influenced by the possible biases of the subjectivity of the person. Many questionnaires are used in order to measure these psychosocial parameters [92]; the most used is by Karasek, which aims to measure stress [107]. Schulte et al. [16] have shown, through a literature review about potential hazards at work, that the most anticipated risks are physical, chemical and biological risks. Scafa et al. [98] have shown, in an industrial use case, a strong relation between well-being at work and perceived workload for the operator. This effect has a strong long-term beneficial impact for companies because of the increase in productivity and decrease in absenteeism. Emergency operations in industry heavily rely on self-control, which is heavily impacted by psychosocial parameters [28].

#### 3.3.2. Integration of Psychosocial Risks in the Decision System

The integration of psychosocial parameters in the organization process is harder because the data are less reliable and the performance indicator may be biased. With the advances of Industry 4.0, work is evolving and the increased interaction with technology has an impact on the well-being of workers [99]. Emotional strain, lack of independence, social relations and the insecurity of the work situation are questioned during this evolution of the workplace in industry. Feedback from workers is needed in order to evaluate psychosocial risks on the workshop floor because there is no objective measure to determine well-being. The implication of workers in the designing process reduces the insecurity risks linked with the integration of new technologies because workers have a better understanding of the human-machine collaboration and have an impact on the development of their tools [90,93]. The integration of human-in-the-loop simulation inside the process control of CPPS objectives is to provide assistance systems in order to benefit from human competencies and capacities, while simultaneously respecting human limits [101]. Climate and ties to the workplace are menaced by the psychosocial factors, which greatly impact the sense of stress and the sense of threat for the worker [13].
line with ergonomics considerations. For this study, an algorithm was developed to analyze the performance of the collaboration in terms of ergonomics, safety, production efficiency and economics. Perruzini et al. [85] proposed an exploration of the different tools used to assess ergonomics and performance at work as well as feedback on the feasibility of the operator 4.0 interface. However, these tools need to be adaptative in order to respond to the cognitive needs of the worker, for example, to assist the ageing workforce with new machine interfaces [89]. The emotional response of workers, the applicability of the method and the needs of large empirical data are the limitations highlighted in the study. To simplify the integration of human error in the decision system, Angeloupou et al. [86] computed a simulation model using performance indicator such as: Experience, Stress, Safety or cognitive complexity of a task. These new management trends in Industry 4.0 are aiming to strengthen the human-machine collaboration; however, this collaboration bring possible occupational risks for the human [87].

### 3.3. *Psychosocial Risks*

Psychosocial constraints at work are an important risk factor in the development of occupational diseases at work [12]. The idea of this paragraph is to list the different methods to evaluate the different psychosocial parameters and their impacts on the well-being and physical risks of the worker. The second is to understand how psychosocial constraints are integrated into industrial organizational and decisional systems.

#### 3.3.1. Psychosocial Parameters Measurement

There are six identified factors in the development of psychosocial risks [4]: intensity and quantity of work, emotional strain, lack of independence, social relations, value conflicts and insecure work situation. These psychosocial risks factors are impacting the perception of the workload for the human and are often associated with stress and possible burnouts. Psychosocial parameters are harder to measure than fatigue and ergonomics assessments because they rely on a questionnaire answered by the worker and are influenced by the possible biases of the subjectivity of the person. Many questionnaires are used in order to measure these psychosocial parameters [92]; the most used is by Karasek, which aims to measure stress [107]. Schulte et al. [16] have shown, through a literature review about potential hazards at work, that the most anticipated risks are physical, chemical and biological risks. Scafa et al. [98] have shown, in an industrial use case, a strong relation between well-being at work and perceived workload for the operator. This effect has a strong long-term beneficial impact for companies because of the increase in productivity and decrease in absenteeism. Emergency operations in industry heavily rely on self-control, which is heavily impacted by psychosocial parameters [28].

#### 3.3.2. Integration of Psychosocial Risks in the Decision System

The integration of psychosocial parameters in the organization process is harder because the data are less reliable and the performance indicator may be biased. With the advances of Industry 4.0, work is evolving and the increased interaction with technology has an impact on the well-being of workers [99]. Emotional strain, lack of independence, social relations and the insecurity of the work situation are questioned during this evolution of the workplace in industry. Feedback from workers is needed in order to evaluate psychosocial risks on the workshop floor because there is no objective measure to determine well-being. The implication of workers in the designing process reduces the insecurity risks linked with the integration of new technologies because workers have a better understanding of the human-machine collaboration and have an impact on the development of their tools [90,93]. The integration of human-in-the-loop simulation inside the process control of CPPS objectives is to provide assistance systems in order to benefit from human competencies and capacities, while simultaneously respecting human limits [101]. Climate and ties to the workplace are menaced by the psychosocial factors, which greatly impact the sense of stress and the sense of threat for the worker [13].
### 3.4. Synthesis on Health-Related Parameters Measurement in the Literature

After reviewing the various measurement methodologies used for assessing the health-related parameters for human operators in industry, Table 3 introduces a classification of these works according to the methodology they are using and the health objectives they are addressing.

<table><caption>**Table 3.** Synthesis of health parameters, measurement methodologies and health objectives.</caption><thead><tr><th rowspan="2">References</th><th colspan="5">Measurement Methodologies</th><th colspan="3">Health Objectives</th></tr><tr><th>Observation</th><th>Questionnaires</th><th>Measurement</th><th>Simulation</th><th>Short-Term Fatigue</th><th>Long-Term Strain</th><th>Psychosocial Risks</th></tr></thead><tbody><tr><td>[19]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[20]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[21]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[22]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[23]</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td><td></td></tr><tr><td>[24]</td><td></td><td></td><td></td><td>X</td><td>X</td><td></td><td></td></tr><tr><td>[25]</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[26]</td><td>X</td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[28]</td><td>X</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>[29]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[30]</td><td>X</td><td>X</td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[31]</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[32]</td><td></td><td></td><td>X</td><td>X</td><td>X</td><td></td><td></td></tr><tr><td>[33]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[56]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[57]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[58]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[56]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[59]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[60]</td><td>X</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>[11]</td><td></td><td>X</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>[61]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[62]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[63]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[64]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[65]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[66]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[67]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[68]</td><td></td><td></td><td>X</td><td></td><td></td><td>X</td><td></td></tr><tr><td>[69]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[70]</td><td>X</td><td></td><td>X</td><td></td><td></td><td>X</td><td></td></tr><tr><td>[71]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[72]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[73]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr></tbody></table>
### 3.4. Synthesis on Health-Related Parameters Measurement in the Literature

After reviewing the various measurement methodologies used for assessing the health-related parameters for human operators in industry, Table 3 introduces a classification of these works according to the methodology they are using and the health objectives they are addressing.

<table><caption>**Table 3.** Synthesis of health parameters, measurement methodologies and health objectives.</caption><thead><tr><th rowspan="2">References</th><th colspan="5">Measurement Methodologies</th><th colspan="3">Health Objectives</th></tr><tr><th>Observation</th><th>Questionnaires</th><th>Measurement</th><th>Simulation</th><th>Short-Term Fatigue</th><th>Long-Term Strain</th><th>Psychosocial Risks</th></tr></thead><tbody><tr><td>[19]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[20]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[21]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[22]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[23]</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td><td></td></tr><tr><td>[24]</td><td></td><td></td><td></td><td>X</td><td>X</td><td></td><td></td></tr><tr><td>[25]</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[26]</td><td>X</td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[28]</td><td>X</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>[29]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[30]</td><td>X</td><td>X</td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[31]</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td><td></td></tr><tr><td>[32]</td><td></td><td></td><td>X</td><td>X</td><td>X</td><td></td><td></td></tr><tr><td>[33]</td><td></td><td></td><td>X</td><td></td><td>X</td><td></td><td></td></tr><tr><td>[56]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[57]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[58]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[56]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[59]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[60]</td><td>X</td><td>X</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>[11]</td><td></td><td>X</td><td></td><td></td><td></td><td>X</td><td></td></tr><tr><td>[61]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[62]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[63]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[64]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[65]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[66]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[67]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[68]</td><td></td><td></td><td>X</td><td></td><td></td><td>X</td><td></td></tr><tr><td>[69]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[70]</td><td>X</td><td></td><td>X</td><td></td><td></td><td>X</td><td></td></tr><tr><td>[71]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[72]</td><td></td><td></td><td></td><td>X</td><td></td><td>X</td><td></td></tr><tr><td>[73]</td><td></td><td></td><td>X</td><td>X</td><td></td><td>X</td><td></td></tr></tbody></table>
<table><caption>Table 3. *Cont.*</caption><thead><tr><th rowspan="2">References</th><th colspan="4">Measurement Methodologies</th><th colspan="3">Health Objectives</th></tr><tr><th>Observation</th><th>Questionnaires</th><th>Measurement</th><th>Simulation</th><th>Short-Term Fatigue</th><th>Long-Term Strain</th><th>Psychosocial Risks</th></tr></thead><tbody><tr><td>[39]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[90]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[91]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[92]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[93]</td><td>X</td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[94]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[95]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[96]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr></tbody></table>

A cell marked with an X indicates the measurement methodologies or health objectives expressed in each reference.

Referring to our study in [108] and from the analysis of the measurement methodologies presented before, it appears that these methodologies can be classified into four different categories: observation methods, where the impact of the workload on the operator is assessed by a human observer; questionnaires, designed to obtain non-measurable data from the feeling of the operators; direct measurements, for example, wearable effort sensors; and simulation models, for example, through virtual reality or physical models.

This classification exhibits the interest of using sensors and direct measurement for short-term fatigue. Sometimes coupled with simulation models, these very precise data allow an assessment of the mechanical efforts felt by operators. Surprisingly, very few works confront these models to the actual health of the operators, for example, via some questionnaires.

Due to time constraints and the difficulty of reproducing long-term physical strains in the laboratory, studies related to this objective usually rely on simulation techniques. Again, there is a lack of model validation that can be observed from a global point of view, and the actual effects on the operators are generally not assessed.

Finally, as expected, psychosocial risks are usually assessed by using questionnaires and observation methods. This result was expected due to the lack of availability of measurement methodologies and corresponding devices. However, it also has to be noted that simulation models are not available either. Among others, this is due to the lack of models describing the evolution of such risks in the literature.

## 4. Discussion

The results from the systematic literature review confirm that the health-related issues in industry have been gaining interest in research communities over the last few years. The global scope of questions is addressed in a balanced way, and while many solutions are already provided, a lot of research questions still remain.

Short-term fatigue measurement has already been a key research question in professional sport, and the tools used to measure energy expenditure can be suitable to the industrial workplace. This understanding of the human fatigue facilitates its measurement and the integration in industrial systems. Digitalization of these processes, with the virtual reproduction of a task, is a new step developed to predict and to reduce the physical-straining situation in the workplace. The motion capture technology used to virtualize a task relies on high precision sensors that evaluate the position and force exerted on the joints and muscles of the worker. This use of body sensors may create a measuring bias, as the worker may adapt their movements because they are being monitored and wearing equipment with sensors.

Physical fatigue has been well documented over the past decades, but cognitive strain is a relatively new problem within the workplace. Decision taking and reaction time can
<table><caption>Table 3. *Cont.*</caption><thead><tr><th rowspan="2">References</th><th colspan="4">Measurement Methodologies</th><th colspan="3">Health Objectives</th></tr><tr><th>Observation</th><th>Questionnaires</th><th>Measurement</th><th>Simulation</th><th>Short-Term Fatigue</th><th>Long-Term Strain</th><th>Psychosocial Risks</th></tr></thead><tbody><tr><td>[39]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[90]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[91]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[92]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[93]</td><td>X</td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[94]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[95]</td><td>X</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>[96]</td><td></td><td>X</td><td></td><td></td><td></td><td></td><td>X</td></tr></tbody></table>

A cell marked with an X indicates the measurement methodologies or health objectives expressed in each reference.

Referring to our study in [108] and from the analysis of the measurement methodologies presented before, it appears that these methodologies can be classified into four different categories: observation methods, where the impact of the workload on the operator is assessed by a human observer; questionnaires, designed to obtain non-measurable data from the feeling of the operators; direct measurements, for example, wearable effort sensors; and simulation models, for example, through virtual reality or physical models.

This classification exhibits the interest of using sensors and direct measurement for short-term fatigue. Sometimes coupled with simulation models, these very precise data allow an assessment of the mechanical efforts felt by operators. Surprisingly, very few works confront these models to the actual health of the operators, for example, via some questionnaires.

Due to time constraints and the difficulty of reproducing long-term physical strains in the laboratory, studies related to this objective usually rely on simulation techniques. Again, there is a lack of model validation that can be observed from a global point of view, and the actual effects on the operators are generally not assessed.

Finally, as expected, psychosocial risks are usually assessed by using questionnaires and observation methods. This result was expected due to the lack of availability of measurement methodologies and corresponding devices. However, it also has to be noted that simulation models are not available either. Among others, this is due to the lack of models describing the evolution of such risks in the literature.

## 4. Discussion

The results from the systematic literature review confirm that the health-related issues in industry have been gaining interest in research communities over the last few years. The global scope of questions is addressed in a balanced way, and while many solutions are already provided, a lot of research questions still remain.

Short-term fatigue measurement has already been a key research question in professional sport, and the tools used to measure energy expenditure can be suitable to the industrial workplace. This understanding of the human fatigue facilitates its measurement and the integration in industrial systems. Digitalization of these processes, with the virtual reproduction of a task, is a new step developed to predict and to reduce the physical-straining situation in the workplace. The motion capture technology used to virtualize a task relies on high precision sensors that evaluate the position and force exerted on the joints and muscles of the worker. This use of body sensors may create a measuring bias, as the worker may adapt their movements because they are being monitored and wearing equipment with sensors.

Physical fatigue has been well documented over the past decades, but cognitive strain is a relatively new problem within the workplace. Decision taking and reaction time can
be measured, thus creating an indicator on operator performance; but, the biggest part of
understanding cognitive constraints requires a subjective analysis of the worker. Cognitive
ergonomics have become part of designing workplaces, in order to facilitate the operations
of workers in a complex workplace.

Long-term strain and the development of occupational musculoskeletal disorders are an important societal and financial problem for industry because of the medical allowances for workers with occupational diseases as well as the costs to recruit and train new workers. Progress on the understanding of these long-term work-related physical risks have been made with the help of evaluation methods, which are either observational, based on a self-evaluation questionnaire or dependent on tools to measure the physical expenditure of the worker. However, this large range of methods makes it difficult to uniformize the measure of a physical constraint. For example, in 2010 Takala et al. [109] had already identified 30 observational methods used to measure long-term physical strain. This huge diversity of measurement methods eases the finding of a good fitting solution for the evaluation of physical constraints, as it can consider the integration of different workplace specificities. However, this large choice also affects the uniformity of a measure, when, for example, the same task is measured at two different physical strain levels. The comparison of the eight main observational methods used to evaluation physical constraints shows that the results produced with these methods may differ considerably [110]. Empirically, industry companies tend to contribute more in developing solutions for long-term physical strain risks such as posture, repetition of tasks and force exerted because these risks represent the major risk of developing occupational diseases. The integration of these long-term risks in the industry organization remains complex because it is nearly impossible to track occupational disease risk for a specific worker over forty years and the risk factors of MSD are too numerous to all be studied in working conditions.

This measurement problem also tends to appear with the measurement of psychosocial
constraints because these constraints are mainly measured with questionnaires directed to
workers. For example, the Karasek questionnaire [107] is the most frequently used in order
to measure stress at work, but it is not often adapted for industrial work life because this
questionnaire is not designed to show an evolution of the health parameters of workers;
thus, new questionnaires have been created by psychologists and health specialists in
order to answer specific problems identified by industrial companies. The psychosocial
risks are tougher to address because they rely more specifically on the perception of the
worker than the objective situation at the workplace. This review shows that prevention
and understanding of the psychosocial risks is key for companies to create a sustainable
workplace to enhance the well-being of workers, which improves both worker productivity
and company image.

This review also highlights the reality that in recent years manufacturing companies
have tended to invest more into research about the well-being of workers and collaborations
with scientists in order to understand the physical, cognitive and psychological risks for
the production worker. Many solutions are being developed with the objective to reduce
these identified risks and to integrate human factors into decision systems. However, most
such studies have a theoretical approach to human factors and well-being in industry as
well as a lack of empirical evidence to support either the measurement, the reduction or
the integration of human factors. It is very likely that the integration of these different
solutions on site remains complicated because it represents important expenses for the
companies with a slow return on investment. Physical assistance is often prioritized
because physical strain represents, for most companies, the key parameter in occupational
musculoskeletal disorders.

The complexity behind these measures is that they rely on an important subjectivity: different measurement methods, the individual sensibility of workers and out-of-work elements. The difficulties for the organization are to take into account these measures and to integrate them into the manufacturing process. To simplify this process, we can
be measured, thus creating an indicator on operator performance; but, the biggest part of
understanding cognitive constraints requires a subjective analysis of the worker. Cognitive
ergonomics have become part of designing workplaces, in order to facilitate the operations
of workers in a complex workplace.

Long-term strain and the development of occupational musculoskeletal disorders are an important societal and financial problem for industry because of the medical allowances for workers with occupational diseases as well as the costs to recruit and train new workers. Progress on the understanding of these long-term work-related physical risks have been made with the help of evaluation methods, which are either observational, based on a self-evaluation questionnaire or dependent on tools to measure the physical expenditure of the worker. However, this large range of methods makes it difficult to uniformize the measure of a physical constraint. For example, in 2010 Takala et al. [109] had already identified 30 observational methods used to measure long-term physical strain. This huge diversity of measurement methods eases the finding of a good fitting solution for the evaluation of physical constraints, as it can consider the integration of different workplace specificities. However, this large choice also affects the uniformity of a measure, when, for example, the same task is measured at two different physical strain levels. The comparison of the eight main observational methods used to evaluation physical constraints shows that the results produced with these methods may differ considerably [110]. Empirically, industry companies tend to contribute more in developing solutions for long-term physical strain risks such as posture, repetition of tasks and force exerted because these risks represent the major risk of developing occupational diseases. The integration of these long-term risks in the industry organization remains complex because it is nearly impossible to track occupational disease risk for a specific worker over forty years and the risk factors of MSD are too numerous to all be studied in working conditions.

This measurement problem also tends to appear with the measurement of psychosocial
constraints because these constraints are mainly measured with questionnaires directed to
workers. For example, the Karasek questionnaire [107] is the most frequently used in order
to measure stress at work, but it is not often adapted for industrial work life because this
questionnaire is not designed to show an evolution of the health parameters of workers;
thus, new questionnaires have been created by psychologists and health specialists in
order to answer specific problems identified by industrial companies. The psychosocial
risks are tougher to address because they rely more specifically on the perception of the
worker than the objective situation at the workplace. This review shows that prevention
and understanding of the psychosocial risks is key for companies to create a sustainable
workplace to enhance the well-being of workers, which improves both worker productivity
and company image.

This review also highlights the reality that in recent years manufacturing companies
have tended to invest more into research about the well-being of workers and collaborations
with scientists in order to understand the physical, cognitive and psychological risks for
the production worker. Many solutions are being developed with the objective to reduce
these identified risks and to integrate human factors into decision systems. However, most
such studies have a theoretical approach to human factors and well-being in industry as
well as a lack of empirical evidence to support either the measurement, the reduction or
the integration of human factors. It is very likely that the integration of these different
solutions on site remains complicated because it represents important expenses for the
companies with a slow return on investment. Physical assistance is often prioritized
because physical strain represents, for most companies, the key parameter in occupational
musculoskeletal disorders.

The complexity behind these measures is that they rely on an important subjectivity: different measurement methods, the individual sensibility of workers and out-of-work elements. The difficulties for the organization are to take into account these measures and to integrate them into the manufacturing process. To simplify this process, we can
question the possibility of defining notation standards to make the physical, cognitive and psychosocial risk evaluations by the workplace more comparable.

Results are also showing that including production operators in the development and ramp-up of new technologies or new production means is mandatory to obtain great user satisfaction and better performance. The acceptance of new technologies is an addressed problem that may occur during Industry 4.0. Workers can perceive the introduction of intelligent systems as a threat to their role in industry, and this fear of replacement is a stress factor and a psychosocial risk that can affect well-being if it is not properly addressed. This operator consideration within industry reduces the impact of the human-machine competition and leads to a better understanding of the collaboration.

With the integration of health parameters in decision systems, we can address the problem of personal data privacy and the ethical aspect of using the personal data of workers in order to improve performance and reduce physical risks. In order to reinforce the consideration of human factors within the organization, workers agree to give access to personal information about health and psychosocial parameters that are often bound to their personal lives outside of the workplace. This information is sensitive because it may discriminate against workers in the workplace at decisive moments of their careers. Recruitments and promotions may be influenced by these private data, creating stress for workers about their physical states. This problem needs to be addressed when creating tools for organizations based on the private data of workers.

**5. Conclusions**

Sustainable development has been an important subject over the last decade in modern industry; thus, integrating human factors into the design and organization of the workplace has become a key subject, with the objectives being improving well-being at work and reducing the risk of workers developing occupational diseases. This importance is highlighted by the significant increase in the number of publications on the subject during the last seven years.

Considering the first research question addressed in this review, a large set of tools and methodologies used to evaluate the health parameters considered for the industry were identified and presented. They consist of sensor device implementation, health questionnaires, ergonomic observations and simulation. The large diversity of tools and methods can be explained by the specificities of each usage. Consequently, this diversity complicates the comparison of working conditions between different industrial situations.

Considering the second research question, the mitigation of the impact of the workload on the condition of operators is targeted by a diminution of the workload itself (both physical and cognitive). In practice, these two measures can be applied: either changing the typology of operations asked to the operator in order to modify their physical or cognitive impacts; or assisting the execution of the operations with the use of technical devices or robots.

Finally, considering the third research question, the implementation of those solutions in an industrial context can be seen as relatively heterogeneous. If assisting solutions, often based on ergonomics studies, are generally efficient and well adopted, organizational solutions such as job rotation are usually a lot more challenging.

Short-term fatigue and long-term strain have been differentiated to understand the different approaches; the first one is mostly based on efforts by the worker while the other one relies on the repetitiveness of the industrial tasks. Solutions to identify and to reduce psychosocial risks in industry have also been screened because of their importance in the development of occupational disease and their role in the well-being of the worker at the workplace.

This review facilitated the identification of three primordial steps: analysis of the situation and highlighting of the operational risk; research and proposal of a solution to reduce this risk; then, implementation and integration into the organizational loop.
question the possibility of defining notation standards to make the physical, cognitive and psychosocial risk evaluations by the workplace more comparable.

Results are also showing that including production operators in the development and ramp-up of new technologies or new production means is mandatory to obtain great user satisfaction and better performance. The acceptance of new technologies is an addressed problem that may occur during Industry 4.0. Workers can perceive the introduction of intelligent systems as a threat to their role in industry, and this fear of replacement is a stress factor and a psychosocial risk that can affect well-being if it is not properly addressed. This operator consideration within industry reduces the impact of the human-machine competition and leads to a better understanding of the collaboration.

With the integration of health parameters in decision systems, we can address the problem of personal data privacy and the ethical aspect of using the personal data of workers in order to improve performance and reduce physical risks. In order to reinforce the consideration of human factors within the organization, workers agree to give access to personal information about health and psychosocial parameters that are often bound to their personal lives outside of the workplace. This information is sensitive because it may discriminate against workers in the workplace at decisive moments of their careers. Recruitments and promotions may be influenced by these private data, creating stress for workers about their physical states. This problem needs to be addressed when creating tools for organizations based on the private data of workers.

## 5. Conclusions

Sustainable development has been an important subject over the last decade in modern industry; thus, integrating human factors into the design and organization of the workplace has become a key subject, with the objectives being improving well-being at work and reducing the risk of workers developing occupational diseases. This importance is highlighted by the significant increase in the number of publications on the subject during the last seven years.

Considering the first research question addressed in this review, a large set of tools and methodologies used to evaluate the health parameters considered for the industry were identified and presented. They consist of sensor device implementation, health questionnaires, ergonomic observations and simulation. The large diversity of tools and methods can be explained by the specificities of each usage. Consequently, this diversity complicates the comparison of working conditions between different industrial situations.

Considering the second research question, the mitigation of the impact of the workload on the condition of operators is targeted by a diminution of the workload itself (both physical and cognitive). In practice, these two measures can be applied: either changing the typology of operations asked to the operator in order to modify their physical or cognitive impacts; or assisting the execution of the operations with the use of technical devices or robots.

Finally, considering the third research question, the implementation of those solutions in an industrial context can be seen as relatively heterogeneous. If assisting solutions, often based on ergonomics studies, are generally efficient and well adopted, organizational solutions such as job rotation are usually a lot more challenging.

Short-term fatigue and long-term strain have been differentiated to understand the different approaches; the first one is mostly based on efforts by the worker while the other one relies on the repetitiveness of the industrial tasks. Solutions to identify and to reduce psychosocial risks in industry have also been screened because of their importance in the development of occupational disease and their role in the well-being of the worker at the workplace.

This review facilitated the identification of three primordial steps: analysis of the situation and highlighting of the operational risk; research and proposal of a solution to reduce this risk; then, implementation and integration into the organizational loop.
In the future, we can expect more research studies on occupational disease caused by industrial working activities. Societal evolutions are pushing the importance of safety at work and human well-being; this dynamic is helping research about human factors at work, which may explain the rapid increase in articles published on this subject. These societal evolutions are key in the research of the development of human factors because they bring an overview of what can be done to help assist workers. An evaluation of the risk of occupational disease from previous work-based tasks by workers could be key to supporting companies in the integration of technological methods to reduce physical and psychosocial risks at work.

**Author Contributions:** Conceptualization, N.M. and O.C.; methodology, N.M., O.C., A.M. and M.-P.S.; validation, O.C., A.M. and M.-P.S.; formal analysis, N.M.; resources, N.M. and O.C.; data curation, N.M.; writing—original draft preparation, N.M.; writing—review and editing, N.M., O.C., A.M. and M.-P.S.; visualization, N.M., O.C., A.M. and M.-P.S.; supervision, O.C.; project administration, O.C.; funding acquisition, N.M. and O.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was financed by Airbus Group and ANRT in the framework of a CIFRE thesis.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Nayyar, A.; Kumar, A. (Eds.) *A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development*; Advances in Science, Technology & Innovation; Springer International Publishing: Cham, Germany, 2020; ISBN 978-3-030-14543-9.

2. Sgarbossa, F.; Grosse, E.H.; Neumann, W.P.; Battini, D.; Glock, C.H. Human factors in production and logistics systems of the future. *Annu. Rev. Control* **2020**, *49*, 295–305. [CrossRef]

3. Battini, D.; Delorme, X.; Dolgui, A.; Sgarbossa, F. Assembly line balancing with ergonomics paradigms: Two alternative methods. *IFAC Pap.* **2015**, *48*, 586–591. [CrossRef]

4. Koukoulaki, T. The impact of lean production on musculoskeletal and psychosocial risks: An examination of sociotechnical trends over 20 years. *Appl. Ergon.* **2014**, *45*, 198–212. [CrossRef] [PubMed]

5. Costa, D.; Pires, F.; Rodrigues, N.; Barbosa, J.; Igrejas, G.; Leitao, P. Empowering Humans in a Cyber-Physical Production System: Human-in-the-loop Perspective. In *Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)*, Taipei, Taiwan, 6–9 May 2019; pp. 139–144.

6. Putnik, G.D.; Ferreira, L.; Lopes, N.; Putnik, Z. What is a Cyber-Physical System: Definitions and models spectrum. *FME Trans.* **2019**, *47*, 663–674. [CrossRef]

7. Kadir, B.A.; Broberg, O.; Conceição, C.S. da Current research and future perspectives on human factors and ergonomics in Industry 4.0. *Comput. Ind. Eng.* **2019**, *137*, 106004. [CrossRef]

8. Bernard, B.P.; Putz-Anderson, V. Musculoskeletal disorders and workplace factors. In *A Critical Review of Epidemiologic Evidence for Work-Related Musculoskeletal Disorders of the Neck, Upper Extremity, and Low Back*; U.S. Department of Health and Human Services: Cincinatti, OH, USA, 1997.

9. Widanarko, B.; Legg, S.; Stevenson, M.; Devereux, J.; Eng, A.; Mannetje, A.T.; Cheng, S.; Douwes, J.; Ellison-Loschmann, L.; McLean, D.; et al. Prevalence of musculoskeletal symptoms in relation to gender, age, and occupational/industrial group. *Int. J. Ind. Ergon.* **2011**, *41*, 561–572. [CrossRef]

10. Fan, Z.J.; Silverstein, B.A.; Bao, S.; Bonauto, D.K.; Howard, N.L.; Spielholz, P.O.; Smith, C.K.; Polissar, N.L.; Viikari-Juntura, E. Quantitative exposure-response relations between physical workload and prevalence of lateral epicondylitis in a working population. *Am. J. Ind. Med.* **2009**, *52*, 479–490. [CrossRef]

11. Edwards, D.J.; Rillie, I.; Chileshe, N.; Lai, J.; Hosseini, M.R.; Thwala, W.D. A field survey of hand-arm vibration exposure in the UK utilities sector. *Eng. Constr. Archit. Manag.* **2020**, *27*, 2179–2198. [CrossRef]

12. González-Muñoz, E.L.; Chaurand, R.Á. Analysis of the Role of Job Stress in the Presence of Musculoskeletal Symptoms, Related with Ergonomic Factors. *Procedia Manuf.* **2015**, *3*, 4964–4970. [CrossRef]

13. Dobrowolska, M.; Ślazyk-Sobol, M.; Flakus, M.; Deja, A. Climate and Ties in Workplace versus Sense of Danger and Stress, Based on Empirical Research in the Aviation Industry. *Sustainability* **2020**, *12*, 5302. [CrossRef]

14. Hartvigsen, J.; Lings, S.; Leboeuf-Yde, C.; Bakketeig, L. Psychosocial factors at work in relation to low back pain and consequences of low back pain; a systematic, critical review of prospective cohort studies. *Occup. Environ. Med.* **2004**, *61*, e2.
In the future, we can expect more research studies on occupational disease caused by industrial working activities. Societal evolutions are pushing the importance of safety at work and human well-being; this dynamic is helping research about human factors at work, which may explain the rapid increase in articles published on this subject. These societal evolutions are key in the research of the development of human factors because they bring an overview of what can be done to help assist workers. An evaluation of the risk of occupational disease from previous work-based tasks by workers could be key to supporting companies in the integration of technological methods to reduce physical and psychosocial risks at work.

**Author Contributions:** Conceptualization, N.M. and O.C.; methodology, N.M., O.C., A.M. and M.-P.S.; validation, O.C., A.M. and M.-P.S.; formal analysis, N.M.; resources, N.M. and O.C.; data curation, N.M.; writing—original draft preparation, N.M.; writing—review and editing, N.M., O.C., A.M. and M.-P.S.; visualization, N.M., O.C., A.M. and M.-P.S.; supervision, O.C.; project administration, O.C.; funding acquisition, N.M. and O.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was financed by Airbus Group and ANRT in the framework of a CIFRE thesis.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Nayyar, A.; Kumar, A. (Eds.) *A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development*; Advances in Science, Technology & Innovation; Springer International Publishing: Cham, Germany, 2020; ISBN 978-3-030-14543-9.

2. Sgarbossa, F.; Grosse, E.H.; Neumann, W.P.; Battini, D.; Glock, C.H. Human factors in production and logistics systems of the future. *Annu. Rev. Control* **2020**, *49*, 295–305. [CrossRef]

3. Battini, D.; Delorme, X.; Dolgui, A.; Sgarbossa, F. Assembly line balancing with ergonomics paradigms: Two alternative methods. *IFAC Pap.* **2015**, *48*, 586–591. [CrossRef]

4. Koukoulaki, T. The impact of lean production on musculoskeletal and psychosocial risks: An examination of sociotechnical trends over 20 years. *Appl. Ergon.* **2014**, *45*, 198–212. [CrossRef] [PubMed]

5. Costa, D.; Pires, F.; Rodrigues, N.; Barbosa, J.; Igrejas, G.; Leitao, P. Empowering Humans in a Cyber-Physical Production System: Human-in-the-loop Perspective. In *Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)*, Taipei, Taiwan, 6–9 May 2019; pp. 139–144.

6. Putnik, G.D.; Ferreira, L.; Lopes, N.; Putnik, Z. What is a Cyber-Physical System: Definitions and models spectrum. *FME Trans.* **2019**, *47*, 663–674. [CrossRef]

7. Kadir, B.A.; Broberg, O.; Conceição, C.S. da Current research and future perspectives on human factors and ergonomics in Industry 4.0. *Comput. Ind. Eng.* **2019**, *137*, 106004. [CrossRef]

8. Bernard, B.P.; Putz-Anderson, V. Musculoskeletal disorders and workplace factors. In *A Critical Review of Epidemiologic Evidence for Work-Related Musculoskeletal Disorders of the Neck, Upper Extremity, and Low Back*; U.S. Department of Health and Human Services: Cincinatti, OH, USA, 1997.

9. Widanarko, B.; Legg, S.; Stevenson, M.; Devereux, J.; Eng, A.; Mannetje, A.T.; Cheng, S.; Douwes, J.; Ellison-Loschmann, L.; McLean, D.; et al. Prevalence of musculoskeletal symptoms in relation to gender, age, and occupational/industrial group. *Int. J. Ind. Ergon.* **2011**, *41*, 561–572. [CrossRef]

10. Fan, Z.J.; Silverstein, B.A.; Bao, S.; Bonauto, D.K.; Howard, N.L.; Spielholz, P.O.; Smith, C.K.; Polissar, N.L.; Viikari-Juntura, E. Quantitative exposure-response relations between physical workload and prevalence of lateral epicondylitis in a working population. *Am. J. Ind. Med.* **2009**, *52*, 479–490. [CrossRef]

11. Edwards, D.J.; Rillie, I.; Chileshe, N.; Lai, J.; Hosseini, M.R.; Thwala, W.D. A field survey of hand-arm vibration exposure in the UK utilities sector. *Eng. Constr. Archit. Manag.* **2020**, *27*, 2179–2198. [CrossRef]

12. González-Muñoz, E.L.; Chaurand, R.Á. Analysis of the Role of Job Stress in the Presence of Musculoskeletal Symptoms, Related with Ergonomic Factors. *Procedia Manuf.* **2015**, *3*, 4964–4970. [CrossRef]

13. Dobrowolska, M.; Ślazyk-Sobol, M.; Flakus, M.; Deja, A. Climate and Ties in Workplace versus Sense of Danger and Stress, Based on Empirical Research in the Aviation Industry. *Sustainability* **2020**, *12*, 5302. [CrossRef]

14. Hartvigsen, J.; Lings, S.; Leboeuf-Yde, C.; Bakketeig, L. Psychosocial factors at work in relation to low back pain and consequences of low back pain; a systematic, critical review of prospective cohort studies. *Occup. Environ. Med.* **2004**, *61*, e2.
15. Gualtieri, L.; Rauch, E.; Vidoni, R. Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review. *Robot. Comput. Integr. Manuf.* **2021**, *67*, 101998. [CrossRef]

16. Schulte, P.A.; Streit, J.M.K.; Sheriff, F.; Delclos, G.; Felknor, S.A.; Tamers, S.L.; Fendinger, S.; Grosch, J.; Sala, R. Potential Scenarios and Hazards in the Work of the Future: A Systematic Review of the Peer-Reviewed and Gray Literatures. *Ann. Work Expo. Health* **2020**, *64*, 786–816. [CrossRef]

17. Mahmoudabadi, M.Z. Shared Representation of Work-Related Musculoskeletal Risk Factors and Comparison of Assessment Methods: An Experimental Study in the Truck Manufacturing Industry. Ph.D. Thesis, Universite d'Angers, Angers, France, 2015.

18. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. *BMJ* **2021**, 372, n71. [CrossRef]

19. Pistolesi, F.; Lazzerini, B. Assessing the Risk of Low Back Pain and Injury via Inertial and Barometric Sensors. *IEEE Trans. Ind. Inform.* **2020**, *16*, 7199–7208. [CrossRef]

20. Ranavolo, A.; Ajoudani, A.; Cherubini, A.; Bianchi, M.; Fritzsche, L.; Iavicoli, S.; Sartori, M.; Silvetti, A.; Vanderborght, B.; Varrecchia, T.; et al. The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics. *Sensors* **2020**, *20*, 5750. [CrossRef]

21. Conforti, I.; Mileti, I.; Panariello, D.; Caporaso, T.; Grazioso, S.; Del Prete, Z.; Lanzotti, A.; Di Gironimo, G.; Palermo, E. Validation of a novel wearable solution for measuring L5/S1 load during manual material handling tasks. In Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Rome, Italy, 3–5 June 2020; pp. 501–506.

22. Ali, S.E.; Khan, A.N.; Zia, S.; Mukhtar, M. Human Activity Recognition System using Smart Phone based Accelerometer and Machine Learning. In Proceedings of the 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, Indonesia, 7–8 July 2020; pp. 69–74.

23. Maczewska, A.; Polak-Sopinska, A.; Wisniewski, Z. Computer-Aided Occupational Risk Assessment of Physical Workload in the Logistics 4.0. In *Advances in Human Factors, Business Management and Leadership*; Kantola, J.I., Nazir, S., Eds.; Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Germany, 2020; Volume 961, pp. 378–390, ISBN 978-3-030-20153-1.

24. Battini, D.; Finco, S.; Sgarbossa, F. Human-Oriented Assembly Line Balancing and Sequencing Model in the Industry 4.0 Era. In *Scheduling in Industry 4.0 and Cloud Manufacturing*; Sokolov, B., Ivanov, D., Dolgui, A., Eds.; International Series in Operations Research & Management Science; Springer International Publishing: Cham, Germany, 2020; Volume 289, pp. 141–165, ISBN 978-3-030-43176-1.

25. Madonna, M.; Monica, L.; Anastasi, S.; Di Nardo, M. Evolution of Cognitive Demand in the Human-Machine Interaction Integrated with Industry 4.0 Technologies. *Wit Trans. Built Environ* **2019**, *189*, 13–19.

26. Widodo, L.; Daywin, F.J.; Nadya, M. Ergonomic risk and work load analysis on material handling of PT. XYZ. In *IOP Conference Series: Materials Science and Engineering*; IOP Publishing: Bristol, UK, 2019; Volume 528, p. 012030.

27. Pradani, W.R.; Rahayu, M.; Martini, S.; Kurniawan, M.I. Design of Wood Pellets Carrier using Ergonomic Function Deployment (EFD) Approach to Increase Productivity of Work: A Research at PTPN VIII Ciater. In *IOP Conference Series: Materials Science and Engineering*; IOP Publishing: Bristol, UK, 2019; Volume 528, p. 012011.

28. Longo, F.; Nicoletti, L.; Padovano, A. Modeling workers' behavior: A human factors taxonomy and a fuzzy analysis in the case of industrial accidents. *Int. J. Ind. Ergon.* **2019**, *69*, 29–47. [CrossRef]

29. Horváthová, B.; Dulina, L.; Čechová, I.; Gašo, M.; Bigošová, E. Data collection for ergonomic evaluation at logistics workplaces using sensor system. *Transp. Res. Procedia* **2019**, *40*, 1067–1072. [CrossRef]

30. Ansari, F.; Hold, P.; Sihn, W. Human-Centered Cyber Physical Production System: How Does Industry 4.0 impact on Decision-Making Tasks? In Proceedings of the 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Evanston, IL, USA, 28 June–1 July 2018; pp. 1–6.

31. Stern, H.; Becker, T. Influence of work design elements on work performance and work perception—An experimental investigation. *Procedia CIRP* **2018**, *72*, 1233–1238. [CrossRef]

32. Paviglianiti, A.; Pasero, E. VITAL-ECG: A de-bias algorithm embedded in a gender-immune device. In Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Rome, Italy, 3–5 June 2020; pp. 314–318.

33. Conforti, I.; Mileti, I.; Del Prete, Z.; Palermo, E. Assessing ergonomics and biomechanical risk in manual handling of loads through a wearable system. In Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), Naples, Italy, 4–6 June 2019; pp. 388–393.

34. Grazi, L.; Trigili, E.; Profa, G.; Giovacchini, F.; Crea, S.; Vitiello, N. Design and Experimental Evaluation of a Semi-Passive Upper-Limb Exoskeleton for Workers With Motorized Tuning of Assistance. *IEEE Trans. Neural Syst. Rehabil. Eng.* **2020**, *28*, 2276–2285. [CrossRef] [PubMed]

35. Nardo, M.D.; Forino, D.; Murino, T. The evolution of man–machine interaction: The role of human in Industry 4.0 paradigm. *Prod. Manuf. Res.* **2020**, *8*, 20–34. [CrossRef]

36. Weckenborg, C.; Spengler, T.S. Assembly Line Balancing with Collaborative Robots under consideration of Ergonomics: A cost-oriented approach. *IFAC Pap.* **2019**, *52*, 1860–1865. [CrossRef]

37. Becker, T.; Stern, H. Future Trends in Human Work area Design for Cyber-Physical Production Systems. *Procedia CIRP* **2016**, *57*, 404–409. [CrossRef]
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16. Schulte, P.A.; Streit, J.M.K.; Sheriff, F.; Delclos, G.; Felknor, S.A.; Tamers, S.L.; Fendinger, S.; Grosch, J.; Sala, R. Potential Scenarios and Hazards in the Work of the Future: A Systematic Review of the Peer-Reviewed and Gray Literatures. *Ann. Work Expo. Health* **2020**, *64*, 786–816. [CrossRef]

17. Mahmoudabadi, M.Z. Shared Representation of Work-Related Musculoskeletal Risk Factors and Comparison of Assessment Methods: An Experimental Study in the Truck Manufacturing Industry. Ph.D. Thesis, Universite d'Angers, Angers, France, 2015.

18. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. *BMJ* **2021**, 372, n71. [CrossRef]

19. Pistolesi, F.; Lazzerini, B. Assessing the Risk of Low Back Pain and Injury via Inertial and Barometric Sensors. *IEEE Trans. Ind. Inform.* **2020**, *16*, 7199–7208. [CrossRef]

20. Ranavolo, A.; Ajoudani, A.; Cherubini, A.; Bianchi, M.; Fritzsche, L.; Iavicoli, S.; Sartori, M.; Silvetti, A.; Vanderborght, B.; Varrecchia, T.; et al. The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics. *Sensors* **2020**, *20*, 5750. [CrossRef]

21. Conforti, I.; Mileti, I.; Panariello, D.; Caporaso, T.; Grazioso, S.; Del Prete, Z.; Lanzotti, A.; Di Gironimo, G.; Palermo, E. Validation of a novel wearable solution for measuring L5/S1 load during manual material handling tasks. In Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Rome, Italy, 3–5 June 2020; pp. 501–506.

22. Ali, S.E.; Khan, A.N.; Zia, S.; Mukhtar, M. Human Activity Recognition System using Smart Phone based Accelerometer and Machine Learning. In Proceedings of the 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, Indonesia, 7–8 July 2020; pp. 69–74.

23. Maczewska, A.; Polak-Sopinska, A.; Wisniewski, Z. Computer-Aided Occupational Risk Assessment of Physical Workload in the Logistics 4.0. In *Advances in Human Factors, Business Management and Leadership*; Kantola, J.I., Nazir, S., Eds.; Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Germany, 2020; Volume 961, pp. 378–390, ISBN 978-3-030-20153-1.

24. Battini, D.; Finco, S.; Sgarbossa, F. Human-Oriented Assembly Line Balancing and Sequencing Model in the Industry 4.0 Era. In *Scheduling in Industry 4.0 and Cloud Manufacturing*; Sokolov, B., Ivanov, D., Dolgui, A., Eds.; International Series in Operations Research & Management Science; Springer International Publishing: Cham, Germany, 2020; Volume 289, pp. 141–165, ISBN 978-3-030-43176-1.

25. Madonna, M.; Monica, L.; Anastasi, S.; Di Nardo, M. Evolution of Cognitive Demand in the Human-Machine Interaction Integrated with Industry 4.0 Technologies. *Wit Trans. Built Environ* **2019**, *189*, 13–19.

26. Widodo, L.; Daywin, F.J.; Nadya, M. Ergonomic risk and work load analysis on material handling of PT. XYZ. In *IOP Conference Series: Materials Science and Engineering*; IOP Publishing: Bristol, UK, 2019; Volume 528, p. 012030.

27. Pradani, W.R.; Rahayu, M.; Martini, S.; Kurniawan, M.I. Design of Wood Pellets Carrier using Ergonomic Function Deployment (EFD) Approach to Increase Productivity of Work: A Research at PTPN VIII Ciater. In *IOP Conference Series: Materials Science and Engineering*; IOP Publishing: Bristol, UK, 2019; Volume 528, p. 012011.

28. Longo, F.; Nicoletti, L.; Padovano, A. Modeling workers' behavior: A human factors taxonomy and a fuzzy analysis in the case of industrial accidents. *Int. J. Ind. Ergon.* **2019**, *69*, 29–47. [CrossRef]

29. Horváthová, B.; Dulina, L.; Čechová, I.; Gašo, M.; Bigošová, E. Data collection for ergonomic evaluation at logistics workplaces using sensor system. *Transp. Res. Procedia* **2019**, *40*, 1067–1072. [CrossRef]

30. Ansari, F.; Hold, P.; Sihn, W. Human-Centered Cyber Physical Production System: How Does Industry 4.0 impact on Decision-Making Tasks? In Proceedings of the 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Evanston, IL, USA, 28 June–1 July 2018; pp. 1–6.

31. Stern, H.; Becker, T. Influence of work design elements on work performance and work perception—An experimental investigation. *Procedia CIRP* **2018**, *72*, 1233–1238. [CrossRef]

32. Paviglianiti, A.; Pasero, E. VITAL-ECG: A de-bias algorithm embedded in a gender-immune device. In Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Rome, Italy, 3–5 June 2020; pp. 314–318.

33. Conforti, I.; Mileti, I.; Del Prete, Z.; Palermo, E. Assessing ergonomics and biomechanical risk in manual handling of loads through a wearable system. In Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), Naples, Italy, 4–6 June 2019; pp. 388–393.

34. Grazi, L.; Trigili, E.; Profa, G.; Giovacchini, F.; Crea, S.; Vitiello, N. Design and Experimental Evaluation of a Semi-Passive Upper-Limb Exoskeleton for Workers With Motorized Tuning of Assistance. *IEEE Trans. Neural Syst. Rehabil. Eng.* **2020**, *28*, 2276–2285. [CrossRef] [PubMed]

35. Nardo, M.D.; Forino, D.; Murino, T. The evolution of man-machine interaction: The role of human in Industry 4.0 paradigm. *Prod. Manuf. Res.* **2020**, *8*, 20–34. [CrossRef]

36. Weckenborg, C.; Spengler, T.S. Assembly Line Balancing with Collaborative Robots under consideration of Ergonomics: A cost-oriented approach. *IFAC Pap.* **2019**, *52*, 1860–1865. [CrossRef]

37. Becker, T.; Stern, H. Future Trends in Human Work area Design for Cyber-Physical Production Systems. *Procedia CIRP* **2016**, *57*, 404–409. [CrossRef]
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50. Stern, H.; Becker, T. Development of a Model for the Integration of Human Factors in Cyber-physical Production Systems. *Procedia Manuf.* **2017**, *9*, 151-158. [CrossRef]

51. Fantini, P.; Tavola, G.; Taisch, M.; Barbosa, J.; Leitao, P.; Liu, Y.; Sayed, M.S.; Lohse, N. Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: Methodology and results. In Proceedings of the IECON 2016 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23-26 October 2016; pp. 5711-5716.

52. Gaham, M.; Bouzouia, B.; Achour, N. Human-in-the-Loop Cyber-Physical Production Systems Control (HiLCP2sC): A Multi-objective Interactive Framework Proposal. In *Service Orientation in Holonic and Multi-Agent Manufacturing*; Borangiu, T., Thomas, A., Trentesaux, D., Eds.; Studies in Computational Intelligence; Springer International Publishing: Cham, Germany, 2015; Volume 594, pp. 315-325, ISBN 978-3-319-15158-8.

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56. Menolotto, M.; Komaris, D.-S.; Tedesco, S.; O'Flynn, B.; Walsh, M. Motion Capture Technology in Industrial Applications: A Systematic Review. *Sensors* **2020**, *20*, 5687. [CrossRef]

57. Greco, A.; Caterino, M.; Fera, M.; Gerbino, S. Digital Twin for Monitoring Ergonomics during Manufacturing Production. *Appl. Sci.* **2020**, *10*, 7758. [CrossRef]

58. Ojstersek, R.; Buchmeister, B.; Herzog, N.V. Use of Data-Driven Simulation Modeling and Visual Computing Methods for Workplace Evaluation. *Appl. Sci.* **2020**, *10*, 7037. [CrossRef]

59. Bruno, F.; Barbieri, L.; Muzzupappa, M. A Mixed Reality system for the ergonomic assessment of industrial workstations. *Int. J. Interact. Des. Manuf.* **IJIDeM** **2020**, *14*, 805-812. [CrossRef]

60. Tutak, M.; Brodny, J.; Dobrowolska, M. Assessment of Work Conditions in a Production Enterprise—A Case Study. *Sustainability* **2020**, *12*, 5390. [CrossRef]

61. Bortolini, M.; Faccio, M.; Gamberi, M.; Pilati, F. Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes. *Comput. Ind. Eng.* **2020**, *139*, 105485. [CrossRef]
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76. Cordella, F.; di Luzio, F.S.; Lauretti, C.; Draicchio, F.; Zollo, L. A biofeedback-based posture correction system for working environments. In Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), Naples, Italy, 4–6 June 2019; pp. 405–409.

77. Bertram, P.; Motsch, W.; Rübel, P.; Ruskowski, M. Intelligent Material Supply Supporting Assistive Systems for Manual Working Stations. *Procedia Manuf.* **2019**, *38*, 983–990. [CrossRef]

78. Mengoni, M.; Ceccacci, S.; Generosi, A.; Leopardi, A. Spatial Augmented Reality: An application for human work in smart manufacturing environment. *Procedia Manuf.* **2018**, *17*, 476–483. [CrossRef]

79. Laudante, E. Industry 4.0, Innovation and Design. A new approach for ergonomic analysis in manufacturing system. *Des. J.* **2017**, *20*, S2724–S2734. [CrossRef]

80. Laudante, E.; Caputo, F. Design and Digital Manufacturing: An ergonomic approach for Industry 4.0. In *Proceedings of the Libro de Actas—Systems & Design: Beyond Processes and Thinking (IFDP-SD2016)*; Universitat Politècnica València: Valencia, Spain, 2016.

81. Grazi, L.; Chen, B.; Lanotte, F.; Vitiello, N.; Crea, S. Towards methodology and metrics for assessing lumbar exoskeletons in industrial applications. In Proceedings of the 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), Naples, Italy, 4–6 June 2019; pp. 400–404.

82. Mark, B.G.; Rauch, E.; Matt, D.T. Study of the impact of projection-based assistance systems for improving the learning curve in assembly processes. *Procedia CIRP* **2020**, *88*, 98–103. [CrossRef]

83. Klippert, J.; Reuter, M.; Conrad, A.-K.; Wannöffel, M.; Schulte, D.; Wienbruch, T.; Kuhlenkötter, B. Learning factory for decent work—An interdisciplinary workshop on MES for worker representatives. *Procedia Manuf.* **2020**, *45*, 55–59. [CrossRef]

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101. Pacaux-Lemoine, M.-P.; Berdal, Q.; Enjalbert, S.; Trentesaux, D. Towards human-based industrial cyber-physical systems. In *Proceedings of the 2018 IEEE Industrial Cyber-Physical Systems (ICPS)*, St. Petersburg, Russia, 15–18 May 2018; pp. 615–620.

102. Longo, F.; Padovano, A.; Umbrello, S. Value-Oriented and Ethical Technology Engineering in Industry 5.0: A Human-Centric Perspective for the Design of the Factory of the Future. *Appl. Sci.* **2020**, *10*, 4182. [CrossRef]

103. Pacaux-Lemoine, M.-P. Designing intelligent manufacturing systems through Human-Machine Cooperation principles: A human-centered approach. *Ind. Eng.* **2017**, *111*, 581–595. [CrossRef]

104. Richert, A.; Shehadeh, M.A.; Muller, S.L.; Schroder, S.; Jeschke, S. Socializing with robots: Human-robot interactions within a virtual environment. In *Proceedings of the 2016 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO)*, Shanghai, China, 8–10 July 2016; pp. 49–54.

105. Phillips, R.O. A review of definitions of fatigue—And a step towards a whole definition. *Transp. Res. Part F Traffic Psychol. Behav.* **2015**, *29*, 48–56. [CrossRef]

106. Otto, A.; Battaïa, O. Reducing physical ergonomic risks at assembly lines by line balancing and job rotation: A survey. *Comput. Ind. Eng.* **2017**, *111*, 467–480. [CrossRef]

107. Karasek, R.A. Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign. *Adm. Sci. Q.* **1979**, *24*, 285. [CrossRef]

108. Murcia, N.; Mohafid, A.; Cardin, O. Evaluation Methods of Ergonomics Constraints in Manufacturing Operations for a Sustainable Job Balancing in Industry 4.0. In *International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing*; Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S., Eds.; Springer International Publishing: Cham, Germany, 2021; pp. 274–285.

109. Takala, E.-P.; Pehkonen, I.; Forsman, M.; Hansson, G.-Å.; Mathiassen, S.E.; Neumann, W.P.; Sjøgaard, G.; Veiersted, K.B.; Westgaard, R.H.; Winkel, J. Systematic evaluation of observational methods assessing biomechanical exposures at work. *Scand. J. Work. Environ. Health* **2010**, *36*, 3–24. [CrossRef] [PubMed]

110. Chiasson, M.-È.; Imbeau, D.; Aubry, K.; Delisle, A. Comparing the results of eight methods used to evaluate risk factors associated with musculoskeletal disorders. *Int. J. Ind. Ergon.* **2012**, *42*, 478–488. [CrossRef]