# Grand challenges in physical neuroergonomics

S. Perrey

► To cite this version:

S. Perrey. Grand challenges in physical neuroergonomics. Frontiers in Neuroergonomics, 2023, 4, 10.3389/fnrgo.2023.1137854. [https://doi.org/10.3389/fnrgo.2023.1137854](https://doi.org/10.3389/fnrgo.2023.1137854)

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OPEN ACCESS

EDITED BY
Hasan Ayaz,
Drexel University, United States

REVIEWED BY
Frederic Dehais,
Institut Supérieur de l'Aéronautique et de
l'Espace (ISAE-SUPAERO), France

*CORRESPONDENCE

Stéphane Perrey

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stephane.perrey@umontpellier.fr

SPECIALTY SECTION

This article was submitted to
Physical Neuroergonomics,
a section of the journal
Frontiers in Neuroergonomics

RECEIVED 04 January 2023

ACCEPTED 30 January 2023

PUBLISHED 16 February 2023

CITATION

Perrey S (2023) Grand challenges in physical neuroergonomics.
Front. Neuroergon. 4:1137854.
doi: 10.3389/fnrgo.2023.1137854

COPYRIGHT

© 2023 Perrey. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Grand challenges in physical neuroergonomics

Stéphane Perrey*

EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France

KEYWORDS

muscle, monitoring, multimodal, neuromechanics, physiology, mobile brain imaging, workload, fatigue

## 1. Introduction

When studying the sensorimotor performance of the brain at work (i.e., the core of neuroergonomics), it is essential to consider the engines of physical work –the muscles– specifically at the behavioral level. The main functions of the human musculoskeletal system are to sustain and modulate force production and provide suitable movement with efficacy. Movement is the most common final output of nervous system activity. Executive behaviors range from whole-body movement, including locomotion, to skilled movement sequences of body parts in realistic environments for workers, such as surgeons performing an operation or sportsmen skiing, shooting, or putting a ball in a hole. French scientist Marey (1894) defined human locomotion in 1894 as a series of physiological actions that are created by human as a whole to move in space in a series of successive moments. His research allowed the establishment of the first rules governing the maneuvers of soldiers, features of physical exercises, and operations carried out by workers. To do so, he used some behavioral methods already belonging to physical neuroergonomics. As outlined in Dehais et al. (2020), the section Physical Neuroergonomics of the journal Frontiers in Neuroergonomics is concerned with “the human brain in control of muscular performance, movement, and brain-body interrelationships in conditions of health, workplace, fatigue, training, injury, and disease states”. Thus, physical neuroergonomics “focuses on human physical capabilities and limitations, pertaining to neuro/physiology and biomechanics responses of the human body, as they relate to physical work” (Karwowski et al., 2003).

Generation and control of force are required to walk, manipulate objects, or play sports. On one hand, the ability to accurately produce the various ranges in force with good timing can be accomplished through modifying the firing properties and recruitment order of motor units. In order to generate force voluntarily, on the other hand, the sensorimotor network, a large-scale brain network, must be able to communicate efficiently with the motor neurons in the spinal cord which are responsible for force generation through muscle contraction (Figure 1). Within this basic framework, the neuromuscular control system exhibits tremendous flexibility and adaptability in response to various (loco)motor tasks, different physical work parameters (e.g., force, load, and velocity), acute muscular fatigue, and detraining, injuries, and diseases. When sharing load among multiple muscles, or even with other people, humans are able to select an optimal pattern of muscle activation that minimizes costs from an optimal control theory perspective (Harris and Wolpert, 1998). Also, the human body has many degrees of freedom to generate basic movements, such as reaching or grasping. Nikolai Bernstein first proposed the idea of muscle synergies to explain how the nervous system simplifies control of a vast number of independent parameters (Bernstein, 1967). Each muscle synergy generates a particular mechanical action, and the flexible combination of muscle synergies is thought to allow a wide repertoire of natural motor behaviors.
Despite its great importance in physical neuroergonomics, how the peripheral and central nervous systems achieve optimal behavior and “cooperate” in actual field/work settings remains poorly understood, particularly within the context of multiple and repetitive operating actions when muscle output performance is challenging. During sustained or repeated muscle contractions, muscle force declines throughout time at a specific rate. This transient loss of work capacity resulting from preceding workload, otherwise called fatigue, is one of the most fundamental biological topics both for research and practical application in workplace, sport, and health domains. Fatigue limits human performance in normal conditions and even more so in disease. Task-induced (neuro)muscular fatigue (i.e., motor performance fatigue) is considered in healthy people as a reversible loss of muscle force during work over time, which occurs as a safety mechanism. The central nervous system is thought to play a ‘protecting’ role to prevent injury or damage by proposing different strategies. Inadequate rest periods do not allow time for proper recovery, which could increase the chance of injury and decrease the productivity of workers, such as athletes. Many

injuries in the workplace and in sports are in this way caused
by overuse. Using a range of outcome measures in order to
achieve a thorough understanding of which factors contribute
to force-generation capability of the muscles according to the
environment is needed in physical neuroergonomics. This will
provide new avenues for developing adapted countermeasures and
workplace environments. One potential application of physical
neuroergonomics could be to inform the design of workstations
and user interfaces (e.g. cockpits) for more intuitive interaction and
to reduce fatigue. From this, two grand challenges, illustrated in
Figure 1, are discussed in the next sections.

## 2. Grand challenge 1: Identifying, monitoring, and classifying the markers of physical workloads

Most people are exposed to demanding workloads during
physical tasks that can increase injury, illness risk, musculoskeletal
disorders, and cause fatigue. Synergistic multimodal approaches to
biomarkers analysis in physical work, profiling, and monitoring
can offer an opportunity to gain insight into an individual's
biomechanical and neurophysiological status. When combined
with other contextual information, these approaches provide
evidence-based guidelines and strategies to reduce the risk of
musculoskeletal disorders and injury in humans, while also
optimizing their whole performance. In this regard, objective,
accurate, repeatable, and relevant simultaneous measurements
of the muscle and brain functions should make it possible
to assess the effectiveness of the work capacity, follow its
progress, or to adapt some specific needs for users. In physical
neuroergonomics, one main challenge is centered on the ability
of the musculoskeletal, cardiopulmonary, and central nervous
systems' responses of people to be assessed and quantified in a non-
invasive and unobtrusive manner to diagnose muscle and motor
performance, and to restore specific functions due to fatigability,
injury, or disease. A multivariate approach at a multidimensional,
neuro-mecano-physiological level to monitor and quantify task
loads in the workplace is thus highly required. This first
technological and methodological challenge is fundamentally one
important to physical neuroergonomics. Built on new emerging
wearable technologies allowing real-time monitoring, a new
neuroergonomics-type framework can be proposed in which
neuro- physiological and mechanical adaptation pathways are
considered altogether.

Connected wearable technologies allow for continuous
monitoring of human physiology and movements during our daily
activities and living. In sports, these markers have found broad
utility in quantifying the training load and modeling physical
performance of athletes (Imbach et al., 2022). In the workplace,
characterization of the association between working postures
and work-related musculoskeletal disorders requires accurate
posture measurement for estimating occupational exposure to
physical risk factors. The external load/demand supported by
one person is often considered as the total (loco)motor and
mechanical stress produced by physical work. The so-called
external load, defined as the work completed, can include measures

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of velocity of movement, distance covered, number of repetitions, magnitude of impacts, acceleration number, and work-specific movements such as reaching and grasping. In many situations, repetitive external loads are imposed on the human body due to its interaction with the environment. Ground reaction force is a relevant indicator of the external force and mechanical effort imposed during locomotion tasks and has been considered the main representative and most adequate measure of the impact received by the lower-extremity musculoskeletal system (Zadpoor and Nikooyan, 2011). Motion capture systems are the most used methods to measure external load based on robust biomechanics knowledge. In the health science industry, precise, traditional motion capture is heavily constrained by complex sensors and acquisition environment. To address these limitations, marker-less techniques have to be prioritized and are now accessible (Nakano et al., 2020).

There is an emerging interest in using sensors to capture fine-
grained physical behaviors in various domains of daily life over long
duration and/or requiring motor skills (open skills sport, surgery,
engineering activities, etc.). With increasingly available wearable
technologies, activity trackers with 3-axial accelerometers used
to assess physical work unobtrusively, objectively, continuously,
and routinely can capture daily activity occurring in real life to
improve physical activity and related physiological (Ferguson et al.,
2022) and sports performance (Imbach et al., 2020) outcomes.
However, a high degree of instrumentation (many IMUs at different
body positions) is not always acceptable in a sports or company
context since it would hinder mobility. In a real work setting,
examining the accuracy performance of the IMU sensors (even
holding with suits) during the completion of multiple work tasks
with different relevant kinematic characteristics is still necessary.
By combining wearable sensors and powerful pattern recognition
techniques, an automatic activity recognition system for workload
monitoring in users could be established (Zhang et al., 2022). Deep
learning approaches have not yet fully reached the field of sensor-
based work activity recognition despite its successes in image and
speech recognition.

Finally, monitoring and quantifying human-body kinematics
during real situations for an extended duration could require
alternative technologies in a more non-invasive and accurate
manner. Skin-interfaced wearable devices can monitor the stresses
on muscles and joints and offer highly localized motion capture
capabilities from multimodal sensors in demanding environments
(Ray et al., 2019). Muscle performance relies not only on
biomechanics markers; understanding the underlying physiology
of motor tasks while operating in environments allows the
personalization of physical work activities, as well as the
identification of potential health risks and maladaptation. In
that direction, electromyographic (EMG) measurements by soft,
stretchable electrode arrays interfaced to the skin can record the
electrical signal associated with muscle activation. Changes in
oxygen levels can provide complementary insights into the function
of the skeletal muscle in both healthy and diseased states. Near-
infrared-spectroscopy (NIRS) can study the dynamics of oxygen
levels in skeletal muscle during physical tasks (Perrey, 2022).
Combined EMG and NIRS offers a more complete window into
neuromuscular functioning that can be collected over a long time
period. Recent developments in miniaturizing and embedding the

devices in clothing garments (Giminiani et al., 2020) suggest that
this modality of localized demand quantification not only has merit
but also could have extended applications in the near future for
physical neuroergonomics.

Importantly, physical neuroergonomics has to be associated
with mental demand and the related fatigue. In the real world, the
amount of mental resources employed by the individual increases
to deal with the task difficulty demand (i.e., the mental workload),
especially when performing complex movement. Dual-task studies
of simultaneous exercise and cognitive tasks demonstrated that
the stability and accuracy of cognitively demanding motor
tasks is affected (Mandrick et al., 2013). Concurrent demanding
physical and mental activities may exacerbate motor performance
fatigue (Srinivasan et al., 2016). When physical demand imposes
constraints that may increase mental demand or increase the
difficulty of the motor task, individuals may have less capacity
to handle these constraints, resulting in movement patterns that
may induce subsequent injury (Burcal et al., 2019). Thus, subtle
neuro- mechanical and physiological information from wearable
sensors combined with advanced machine learning methods can
serve as the basis of monitoring mental and physical workloads and
classification in physical neuroergonomics.

3. Grand challenge 2: Revealing the
multiple neural signatures to physical
workloads

Physical neuroergonomics aims to understand the neural
activities related to human performance in real-world task settings,
meaning that it involves the investigation of several neural
systems interacting together during real-world conditions. While
a substantial role of neural activities in controlling muscle
performance in laboratory settings has been identified, a lack
of research in naturalistic settings remains. In addition, most
physical activities studied in a laboratory setting are isometric,
finger movements, as well as basic whole-body movement (e.g.,
cycling) mimicked on a dedicated ergometer. Thus, the second
grand challenge is related to how we reveal and discriminate the
interaction dynamics either at the muscle or the cortex levels,
as well as between the brain and muscle, to achieve individual
muscle performance outputs on the field and during cooperative
tasks of two or more persons simultaneously. Although changes
in motor unit recruitment mediated by central mechanisms can
be indirectly reflected in EMG signals, it is difficult to analyze
the neural mechanisms behind muscle state at a systemic level
using the single EMG-based method. Rather than focusing on
isolated muscles or cortical areas, functional networks should be
constructed at both the muscle and cerebral levels (Liang et al.,
2021). It will relay how the central nervous system coordinates
the activity of multiple muscles to achieve a variety of behavioral
goals, and addresses the interaction among the involved sub-
systems (muscles, joints, nervous system), instead of analyzing the
response of each sub-system separately. In addition, hyperscanning
paradigms (Liu et al., 2018) provide a valuable platform for
observation of neural signatures of cooperative motor actions from
more than one individual in the real world at the peripheral and
central levels. We propose four levels of investigation in studying
functional networks.

Constructing muscle networks to reveal different connection
patterns of synergistic and antagonistic muscles has become
an emerging method to explore the neuromuscular control
mechanism (Boonstra et al., 2015; Kerkman et al., 2020). Muscle
network analysis quantifies the functional connectivity between
motion-related muscles and is able to identify the frequency
characteristics of specific muscles (De Vries et al., 2016) that are
regulated by common neural inputs. Therefore, muscular network
analysis can reveal the characteristics of functional separation and
integration for the neuromuscular system, which is suitable for
analyzing the neuromuscular control mechanisms behind muscle
state fatigue. With high-density EMG based muscle synergy,
it permits more spatial resolution in the recording of muscle
activities, so that it may be possible to isolate activities of different
within-muscle compartments that are under different controls
of the central nervous system (Geng et al., 2022). Finally, a
new conceptual framework of the neural control of movement
can be considered by merging the concept of common input to
motor neurons and modular control, together with the constraints
imposed by recruitment order (Hug et al., 2023).

During physical activity, not only do the neurophysiological
changes play a role at a peripheral level, but brain activities
among interconnected regions also vary based on the type of
motor tasks in relation to the force, the movement velocity,
and amplitude. In addition, brain areas associated with attention,
motor planning, sensorimotor, and cognitive processing at different
brain regions (frontal, central, and parietal) are altered during
tasks that require a high level of physical exertion. These
adaptive changes occurring during a physical task emphasize
the need for identifying the involved neural systems. Several
advanced methods for deriving relevant information regarding
neural fingerprints at multiple scales should be implemented
in physical neuroergonomics. Discrimination between different
levels of intensities or exertion during physical work could be
reflected in the functional connectivity of the brain network,
mainly in the prefrontal motor and central areas. Further
insight into the physical work-induced functional changes in
brain activation patterns should be explored by mapping
cortical networks during physical tasks rather than assessing
localized brain activation. Functional brain connectivity has the
potential to provide a more global picture of the continuous
temporal evolution of brain activity regarding various physical
scenarios. It allows for detection of physical activity-dependent
network reorganization. Several modeling approaches of functional
brain connectivity based on EEG (Ismail and Karwowski,
2020) and fNIRS (Vergotte et al., 2018) during physical
tasks have been proposed and should benefit the physical
neuroergonomics field.

On the other hand, it has been reported that the coupling
relationship between scalp EEG and EMG in a specific frequency
band can reflect the functional coupling between the motor cortex
and working muscles (Mehrkanoon et al., 2014). The role of this
functional corticomuscular coupling in different frequency bands
during various physical tasks might provide important information
to further reveal the underlying neural mechanisms of muscle state
in health and disease (Cremoux et al., 2017; Fauvet et al., 2021).

Finally, shared brain dynamics assessed with EEG or fNIRS
during joint tasks can be revealed in multiple interacting people
(Babiloni and Astolfi, 2014). The study of the neurophysiological
bases of cooperative behavior is crucial for all of the applications
in which strict collaboration between two or more people
is mandatory for the success of the task. Functional brain
connectivity analyses in examining relationships across activated
regions provide insight on how individuals may process and
interpret information, engage with the task, and their potential
impact on motor functional outcomes. Considering the existing
body of hyperscanning literature, a current challenge in physical
neuroergonomics that needs to be addressed is studying complex
motor behaviors during social interactions occurring in a natural
environment between two or more individuals. For that purpose,
inter-brain synchronization (EEG-fNIRS) with measures at the
cortico-spinal level (EMG hyperscanning) may be used to
further characterize the neural dynamics of physical interactions
between individuals.

4. Conclusion

In an effort to defy simple reductionist explanations, new
appealing inter-disciplinary research areas are necessary for a
better understanding of the amazing ability of the brain to
reorganize in response to muscle contractions brought on by
various physical work. Physical neuroergonomics can provide
some potentially fruitful new directions for combining research
in biomechanics, neurophysiology, physiology, sport sciences,
data science, neuroimaging, and neurosciences reflecting physical
processes. The first grand challenge is focused on the use of small,
ubiquitous sensors and/or innovative methods for performance
tracking of brain-muscle relationships across a variety of physical
domains by taking advantages of state-of-the-art data science
methods. The second grand challenge is related to the development
of methods capable of building an integrated picture of the multi-
scale functional networks within the muscle, the brain, and between
the brain and muscle. All in all, it will have a marked impact on our
understanding of limiting factors of the physical work in healthy
and diseased individuals and in evaluating their relationship with
whole performance, injury risk, and safety concerns. The phrase
"The whole is more than the sum of its parts" aptly defines the
modern concept of synergy visible for each of the sub-challenges
of the section Physical Neuroergonomics.

Author contributions

SP contributed to the manuscript draft, revision, read, and
approved the final submitted version.

Conflict of interest

The author declares that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
## Publisher's note

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