SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 3(4); March (2023), pp. 193–204
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Exploring the Research Landscape of Automated Emotion
Recognition System Adoption in Malaysia: A Systematic
Literature Review
Muhammad Nadzree Mohd Yamin1, Kamarulzaman Ab. Aziz1, Tan Gek Siang1, Nor Azlina Ab. Aziz2
Faculty of Business, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka
Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Bukit Beruang, Melaka

1
2

ARTICLE I N FO
Article History
Received 17 November 2022
Accepted 01 June 2023
Keywords
Emotion recognition system (ERS)
Artificial intelligence (AI)
Affective computing (AC)s
The fourth industrial revolution (IR 4.0)
The fifth industrial revolution (IR 5.0)
Technology adoption

ABSTRACTArtificial intelligence (AI) has revolutionized the way we interact with technology, and one of its
key subcategories is the automated emotion recognition system (ERS). ERS is a technology thatenables AI to recognize human emotional states, and it has the potential to transform humancomputer interaction (HCI) as we move towards the fifth industrial revolution (IR 5.0). ERS hassignificant implications for various sectors, including healthcare, education, and the workplace.
For example, in healthcare, ERS can be used to identify and treat mental health disorders, whilein the workplace, it can be used to monitor employee well-being and improve productivity.
However, the adoption of ERS is crucial for its success, and there is a need to understand thefactors that affect its adoption. This paper aims to explore the landscape of ERS adoption in
Malaysia by conducting a systematic literature review of studies published over the past decade
between 2011 and 2022. The review will focus on understanding the current levels of adoption of
ERS in Malaysia, identifying the factors that hinder or facilitate its adoption, and discussing thepotential benefits and drawbacks of using ERS in different contexts. The findings of this review
will provide insights into the current ERS research landscape in Malaysia and inform future
research on the adoption and use of ERS in the country.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

computer science that allows a computer or machine tolearn and be trained to perform and complete a task or
make decisions, replicating how a human being learns
from experience [3]. AI development can be seen over
the recent years, so much so that the AI recent generation
is predicted to reduce the barrier to communication andadvanced learning with human brain-like working
mechanisms [4].
Over the last decade, researchers have identified the
subpart of AI that can be enhanced further using thecapabilities of machine learning. One of the main
highlights that have been suggested is machines andcomputers identify or learn human emotions [5] to
improve the interaction between human-computer andincrease potential future applications for humancomputer. Furthermore, in order for machines andcomputers to learn about human emotions, it needs a

1. Introduction
Human complex emotions are neurophysical changes
involving thoughts, feelings, behavioral responses anddegree of pleasures and displeasures [1]. Furthermore,[1] added that human emotion will always play a majorrole in communication and interpersonal bonding
between people. People may perceive their emotions
based on their physical attributes of facial expression,
speech expression, and body movement [1], [2]. With
recent technological advancement and changes
experienced globally, technologies have grown to be partof the daily lives of people in helping with tasks, aiding
industries and creating opportunities for the future. Oneof the examples of recent technological advancement is
artificial intelligence (AI). AI is an advancement inCorresponding author’s E-mail: kamarulzaman.aziz@mmu.edu.my193system that enables the required process to learn humanemotion [5], [6] and is specified as an emotion
recognition system (ERS). ERS enables a system that
accepts various modalities of data, learns and allows themachine to recognize the emotion of the human subjects.
Previous researchers specified ERS as originally inthe field of Affective Computing (AC), a field that was
brought forward by [7]. According to [8], AC is an abilityof a computer that relates to, arises from and understands
human behavior [7], [8]. With that being said, AC led tofurther findings of emotion recognition, and it has beensuggested by the previous researcher that AI learning cansignify for further findings of ERS as embedded
technology in various applications [10]. Moreover, ERS
has emerged as one of the attractive areas in the field of
AC and AI with the promises of what the technology canachieve [11]. Such as, to develop robots that can interact
and communicate with humans, equipped with functions
for both understanding human emotions and expressing
human emotions [10].
Outcomes and expectations from the previous studies
show that ERS can be an important technology in themaking based on the advantages offered to individuals,
society, organizations, businesses and industry via
various platforms and applications. For example, ERS in
healthcare [12], ERS in driving assistance in the car [13],ERS in the classroom [14] and ERS in smartwatches as
the latest addition due to the modalities of smartwatches
being the same ERS modalities.
In existing research on ERS, researchers adopted AI
algorithms such as convolutional neural network (CNN)
and deep neural network (DNN) for emotion recognition
based on various data modalities like facial expression,voice intonation, heart signals (electrocardiogram), brain
signal (electroencephalogram) and many others. It is
important to look into the relationship between ERS andits significance in society and industry specifically in theindustrial revolution.
ERS can be seen as embedded technology in various
applications that can be used for professional orindividual day-to-day use. Based on the modalities for
ERS, and the potential applications of ERS to society andthe industry, it is important to look into the relationshipbetween ERS and its significance in society and industry
specifically in the industrial revolution.

In recent technological advancements, AI establishedtrustworthiness enabling the AI systems in the aspect ofbeneficence, non-maleficence, autonomy, justice andexplicability [11], [16]. The uniqueness of AI ischaracterized by the integration of AI capabilities withhuman capabilities [11], [17]. AI systems are designed
to operate with varying levels as well as defined
objectives, predictions and recommendations influenced
by real or virtual environments.
AI technologies offer benefits for businesses andindustries such as the automation of repetitive and timeconsuming tasks that allow humans to focus on highervalue of work [11]. For example, massive restructured
data that was once required by human expertise to further
analysis, such example, data from written reports,
analyzing documents, photos, images and videos can beassisted by AI, with AI considered much faster and more
efficient working in completing tasks in a shorter time[11]. Moreover, AI can integrate thousands of computers[11] and other resources to resolve complex problems.
Therefore, AI capabilities are leveraging the need fortechnological advancement to automate the processfurther. AI can provide innovative solutions based on theprevious example in previous studies towards societies,
individuals and industries. With the addition of ERS as
an emerging technology, it can enhance technology
further by understanding human emotions and behavioral
responses [11].
1.2 The Industrial Revolution
The history of the industrial revolution began in the
18th century with the mechanization known as the First
Industrial Revolution (IR 1.0) which was based onmachines powered by water and steam. Today, we areliving in the era of the Fourth Industrial Revolution (IR4.0) where its main characteristics are digitalisation andintegration of computers in co-operating with humans. IR
4.0 reshapes individuals’ lives and works with digitaltransformation and emerging technologies that grow theindustry further [18].
In IR 4.0, technologies such as artificial intelligence(AI), the internet of things (IoT) and big data services
achieve sustainability and productivity [18]. IR 4.0
concept was mainly focused on the manufacturingindustry to smart manufacturing but has evolved in thelast decade with the involvement of digitaltransformation. The results create the entire industry,
delivery channels become digitization [18], [19]. IR 4.0
is a big part of an individual’s daily life and industry.
However, innovations keep being introduced, soon there
will be Fifth Industrial Revolution (IR 5.0) [20].
The main characteristic of I.R 5.0 that differentiates
it from I.R 4.0 is specialization where machines andcomputers are equipped with the ability to understand

1.1 Artificial Intelligence (AI)AI has transformed from just a theory to tangibility
with recent applications from AI to transform businesses,
industries and societies [11]. Inspired by human
intelligence, AI aims to learn, reason, make decisions likehumans [15]. With years of advancing and refining AI,
technologies or smart devices nowadays are equippedwith AI minimizing the need for human intervention [11].
194human and act accordingly [21], [22]. As suggested by[22], the industry will enable humans and machines towork together by bringing humans back into theworkforce, therefore, IR 5.0 will be seen to unlock thepotential of human-computer interaction (HCI). To
signify human-computer interaction, communication
between human-computer needs to be enabled. For
example in [10], [22], the computer system must have theability to communicate with humans in some forms.
Since the used cases of IR 5.0 are still in theirformative years, manufacturers must actively organize
their way to corporate humans and machines to maximise
the opportunity that can be gained in IR 5.0. Technology
enablers such as ERS may enhance robots and machines
to understand human emotions with the proposition ofcollaborative robots (Cobots), hence, [10] suggestions inthe study can help achieve a better understanding of
human-computer interaction.

of emotion recognition technology might enhance theinteraction between social robots and humans.
In marketing perspective from retail industries, ERS
helps in advertising products. One of the main examples
is through text sentiment analysis. [29] suggested thattext mining provides useful data in identifying emotions
awareness through text. Text mining is a learning-basedalgorithm to describe characteristics of text, such as word
expression based on human sentiments and emotions [29].
Combining with multimodalities such as facialrecognition and body movement, [27] suggested thatretailers will be able to identify their customers emotions
from advertisement and leverage the information to
improve product experience [27].
2. Methodology
This study adopted the systematic literature review(SLR) methodology consistent with prior works [30],[31], [32], [33], [34]. This method is ideal when acomplete, summary of prevalent knowledge is needed[35]. SLR allows the researchers to identify andsynthesise all relevant research in building up to theresearch gap, that led to the research question andresearch objectives respectively [34].
In 2009, an international group of systematic
reviewers, methodologists and journal editors publish thePreferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) and a guideline designed tohelp authors prepare a complete report for SLR [36].
PRISMA consists of three stages, first one is
identification. In this section, keywords or search strings,
criteria and database used will be identified. For thisstudy, the search strings were Emotion Recognition
System, Youth, Malaysia, Readiness, and Acceptance.
The criteria were the OR and AND Boolean operatives[30]. The term Emotion recognition system expandedwith synonyms of Emotion Recognition Technology andEmotion Recognition Applications. The search was doneusing the Lens.org platform. The benefits of using thisplatform are increasingly recognized by researchers [30],[37]. First launched by Cambia in 2000 as Patent Lens,
an Australia-based non-profit organization. In 2013 it
became The Lens and has since become more than just a
patent search platform.
Initially, the search for the Emotion Recognition
System, in general, showed a result of 1,050 publications
found based on The Lens filter. This study limited thedate range from 2011 to 2022 and the document types arejournaled articles, conference proceedings articles anddissertations. In the first identification stage, we removed
any duplicated publications before moving further into
the next stage. Based on the total results, 2 identified
publications were removed before the next stage asshown in Fig.1.

1.3 Real-World Applications of ERS
Although in recent years developing systems torecognize human emotions and potential applications ofautomated emotion recognition functions have beensuggested by previous researchers, there are still limited
examples of actual real-world applications. Nevertheless,the continuing industrial revolution and its technologicaladvancements create more opportunities and possibilities
for ERS to be applied.
One of the main highlights that benefit from ERS isin healthcare industries. [23], [24], [25] developed themodalities of ERS and suggested their applications inhealthcare. Recently [26] highlights ERS in the diagnosis
of autism by allowing medical experts to investigate
patients’ emotions. Specifically, the researchers use
facial recognition combined with electrocardiogram(ECG) to identify the six basic emotions. One of the main
reasons is autism patients specifically amongst children
are difficult to observe and accurately recognize theiremotions. Thus, ERS helps the medical team to analyseand understand better the emotions of their patients.
Furthermore, ERS has been suggested to provide better
health services quality assurance by facilitating decision
making throughout the Covid-19 pandemic by allowing
safe monitoring and higher emotion awareness among
the practitioners [27], [28].
Moreover, [27] suggested more real-world
applications start to benefit from ERS modalities during
the Covid-19 outbreak in 2020. For example, online
learning classes using online platform were enhancedwith emotion recognition technology and led to more
effective planning of the lessons content [27], [14].
Furthermore, according to [27], social robots have risen
to facilitate limitations due to covid restrictions. Social
robots work and interact with humans, therefore, features
195

The next stage is screening, before narrowing it down
to retrieval and eligibility. In this stage, we screened the
1,048 publications by language (selecting papers written
in the English language only) and relevance (selecting
papers on ERS in Malaysia only). Therefore 2 papers
have been excluded because not in the English language.
Secondly, since the collection also included technicalpapers on ERS from the perspective of computer scienceand informatics, we further filter the papers based on thedetails of the title and abstract. Those found not focusing
on ERS were excluded. Hence, 267 publications were
excluded. Thus, the total records that were sought for
retrieval such as in Fig.1, were narrowed down to 779.
Then, in the retrieval stage, we need to ensure therecords are accessible. A total of 156 papers wereidentified as not accessible due to subscription
restrictions. Due to budget restrictions, it was notpossible to purchase subscriptions or individual papers.
The remaining records were confirmed to be accessible
either under the existing institutional subscriptions or via
open access. Ultimately, 623 papers remained for
retrieval and were included in the final eligibility
screening.

of records confirmed to be included in this SLR is 37.
The following section reports the results and analysis of
the final set.
3. Results and Analysis
This section reports the descriptive analysis results of
the included 37 records, focusing on the document type,year of publications, subject area, keywords related to thestudies and most cited.
3.1 Document TypeThe collected data has been classified based on three
categories journal article, conference proceedings article
and dissertation. These types of research works are
selected for the quality control process normally in place
before publication. Specifically, the works go through astringent review process by subject matter experts toverify the quality of the work. Moreover, the journal
article and conference proceedings article are chosen due
to journal has a better structure while the conference isshort and precise that has been used in the conference[38].
Based on Table 1, the majority of the publications arejournal articles (20; 54.05%) and the remaining areconference proceedings articles (17; 45.95%). The wasnone for the dissertation category suggesting there ispotential for postgraduate research in this area.
Table 1. Document Type
Document Type Number
of % (N=37)
publications
Journal Article 20
54.05%
Conference
Proceedings
Article1745.95%

Dissertation

0

0%

TOTAL

37

100

3.2 Year of Publication
This study reviewed a decade worth of works fromthe year 2011 to 2022, and the returned publications foreach year are different. There is no definitive trend
according to the number of publications over the decade(see Fig.2). It can be said that there had been a steadyFig.1.Prisma Flow Diagram
The final screening stage is eligibility according to
the scope of the SLR. Here only works reporting from the
Malaysian context are the records of interest. A total of
586 records were identified to be out of scope and thus
not eligible for inclusion in this study. The final number
196

Artificial Intelligence
Information Systems
Biotechnology
Computational
computers
Environmental Science
Social Science
Human-Computer
Interaction
Material Science
Software Analytics
TOTAL

number of conference papers each year, but a relatively
increasing trend can be seen for journal articles.

4
4
3
3

10.81%
10.81%
8.11%
8.11%

2
1
1

5.41%
2.70%
2.70%

1
1
37

2.70%
2.70%
100

3.4. Most Cited
Fig.2. Year of publications

Next, the retrieved records that were included in thestudies were analyzed in terms of citation. According to
the analytics from Lens.org, the 37 publications
collectively garnered 510 scholarly citations. Table 3
shows the top 10 most cited works in the collection.
Ranked 1st, the paper by [40] is also one of the earliest
publications in the retrieved records, thus it is expected
to have a high citation. Furthermore, [40] mostly
discussed the modalities that can be used for ERS. Suchexample, the electrocardiogram (ECG) is one of themodalities for ERS. Since the paper is a review, it
provides a useful compilation of related works, and thisalso contributes towards a higher citation number.

From Fig.2, we see that in the range of 2011 towards
2014 the field of affective computing, artificial
intelligence and machine learning was attracting theinterest of the researcher to find solutions in creating ERS,
however, from 2015 to 2019, the number of publications
was not constant, however, 2019 to 2022, the number ofpublications was increasing again. The upswing in thepast 3 years can be due to the technologicaladvancements that enable researchers to test ERS basedon the modalities in the real world.
3.3. Field of Research

Table 3. Top 10 most cited

Next, the collected records were analyzed according
to their field of research or subject matter (see Table 2).
The highest ranking area of study is computer science
with a total of 9 articles or 24.32%. Secondly, isengineering with 7 articles or 18.92%.
The majority of the discussions focused on thepotential applications, benefits, implementation or
deployment, safety, and risk of ERS. However, out of the
37 articles, there is only one research truly within thefield of social science; from [39] regarding the awarenessand readiness of Malaysian University students for ERS.
Arguably, the implications of the study can be extended
from the context of Malaysian University students to
Malaysian youths in general.

Authors
Jerritta et al.
Murugappan&
Murugappan
Seng et al.

Rashid et al.

Jerritta et al.,Table 2. Subject Matter
Field
Computer Science
Engineering

Frequency
97%(n=37)24.32%
18.92%

Bakhtiyari &HusainWani et al.

197

Title
Physiologicalsignals
based human emotion
recognition: A Review
Human
emotionclassification
using
wavelet transform andKNN
A combine rule based andmachine learning audiovisualemotionrecognition approachHumanemotionrecognition from videos
using spatio temporal andaudio features
Electrocardiogram based
emotionrecognition
system using empiricalmode decomposition anddiscrete fourier transform
Fuzzy
model
of
dominance emotions inaffective computing
A comprehensive review

Year
2011

Cites
223

2011

52

2018

43

2012

37

2013

32

2014

22

2021

21

Chew et al.

Jerritta et al.

Hasnul et al.

ofspeechemotionrecognition systems
Audio
emotionrecognition system using
parallel classifiers andaudio feature analyzer
Emotion detection fromQRS Complex of ECG
signalsusing
hurst
exponent from different
age groups
Electrocardiogram based
emotionrecognition
applications
inhealthcare : A Review

2011

13

2013

10

2021

electroencephalography (EEG), electrocardiography(ECG) and photoplethysmography (PPG). EEG isfrequently used in neuroscience, neural engineering, andbiomedical engineering to measure human brain signals
through the electromagnetic behaviour of specificcomponents [43], [44], [45]. EEG is most likely thefavourite for obtaining high-accuracy data for automatedemotions recognitions because EEG processes use thesame concept as AI systems that used convolutional
neural networks for machine learning and deep machinelearning [46], [40]. ECG is commonly used by previous
researchers in the field of AC due to its potential adoption
using a wearable device. In a previous study by [12],ECG was used to detect stress and the benefit ofmonitoring a patient’s emotional stress condition toensure that a negative tendency is not triggered. Next for
EEG, is photoplethysmography (PPG). PPG has been
said to be more practical and suitable in real-life use
compared to EEG, in combination with ECG as
multimodal, it can be implemented as a “wearable device
that can collect signals from a person withoutcompromising comfort and privacy” [46], [47]. For
instance, current technology devices such as asmartwatch have a built-in ECG to detect stress levels
and blood pressure, consequently identifying temporalemotional states.

9

From Table 3, it can be observed that the earlier works
tend to focus on the technical development of ERS; fromdeveloping modalities to establishing the system. Morerecent works such as [12], classified the applications thatcan benefit ERS such as the ECG-based ERS for
healthcare applications. Meanwhile, [41] reviewed
works on speech-based ERS.
4. Discussion
The trend for ERS has been increasing, although
there is a gap between time and studies based on Fig.2.
However, the momentum of the publications returned has
risen from 2019 to 2022. ERS is not seen as a newtechnology, instead, the previous researcher suggested
ERS is seen as an embedded technology through existing
devices and technologies [42]. Given the advantages andcontinued advancement of AI, machine learning as well
as deep learning, ERS has the potential to be the enablerof new features in various applications that utilize
recognition of human emotions [43].
The following sections discuss the key insights
derived from the retrieved 37 publications. Specifically,
the discussion focused on modalities, applications of
ERS, research gaps identified, limitations, and future
directions for research in ERS.

4.1.2 Physical Modalities
For physical modalities, the modalities that have been
widely used are facial recognition and speech recognition.
Facial recognition has gained popularity amongst ERSpractitioners due to diverse applications that can beapplied in the real world such as marketing purposes,
security supervision, online class and gaming experience[48]. Facial expression can determine the six basic
emotions, namely, sad, anger, happiness, fear andsurprise. Additionally, facial expressions can be used todetect disgust, normality, and drowsiness [49]. Speech
recognition as modalities of ERS is capable of detecting
human speech and interpreting conditions and emotions
for real-life applications [45], [50]. According to [51],speech recognition is defined based on someone’s voice,the computer can learn what condition they are in,therefore concluding what is the emotion of that person.
In [41], a review of the speech emotion recognition
system shows that speech recognition detects andclassify emotion recognition system, only the inability tohandle the real-time problem and different scenario that

4.1. Modalities
ERS can be identified through various input data
modalities, such example, physiological modalities,
psychological modalities and data mining modalities.
These modalities have been majorly developed in thefield of computer science, engineering, artificial
intelligence, information systems, biotechnology andcomputational computers.

4.1.1 Physiological Modalities
In physiological modalities, it is consisting of198

affect human emotions as human speech is vary based on
culture.

understanding of human emotions towards intangibles
products such as music or movies [54]. In addition, with
virtual reality (VR) technology in recent years, thedeveloper can enhance the game further with real-time
ERS usage towards understanding the player.

4.2. Applications
Based on the conducted SLR, some papers presented
the potential applications that can be achieved throughindividuals, society and industries. Firstly, the education
system can be improved with the adoption of ERS [53].
Emotion has a significant influence on the relationshipwith a performance from the perspective of a learner andinstructor as described by [14], and students with positive
emotions led to increasing student interest, and student
performance and “have higher chances at success”. [14]their study suggested a webcam inside a computerlaboratory and using a facial recognition modality toidentify students’ moods and the results show significant
differences in the student’s moods.
From an individual perspective, emotions can affectdriving [13]. This is due to emotions’ relationship withfocus. For example, anger can lead to poor focus. A poor
focus led to an increasing number of accidents, therefore,
it is significant for ERS to be embedded in cars fordriving safety [13]. Furthermore, [13] used a driving
simulation, where a virtual driving experiment was
conducted to test ECG-based ERS in identifying humanemotions while driving.
In [12], ERS was implemented using ECG in thehealthcare industry to “reduce stress and promoterelaxation”. The ECG-based ERS helps a person withmental stress to identify negative and positive emotions.
Furthermore, ERS can be suggested as a supporting aid
for people with certain disorders such as down syndrome,autism, and even elderly people. It will be using facial
expressions in real-time video as automated emotion
recognition and a computer advisor that advises on how
we can react appropriately [54], [59].
ERS can be potential in marketing strategy. Suchexample in brand awareness, product acceptance can beanalyzed based on the response of human facial
expressions in a live launch or video and images [54]. For
example, a small group of people in a room are
introduced to a product, and their reactions through facial
expressions will be recorded and evaluated by ERS
whether they are happy, disgusted, or anxious.
In another aspect, in video games, the developer ofgames has an objective which is to “fulfil his or her dream”
while playing games through immersive gameplay,interesting storyline and intensive graphics [55], [56].
Implementation of emotion detection can improveengagement between entertainment agents and end-user
by alternatively using multimodalities such as speech
recognition and EEG in the controller which can improve

4.3. Gaps
With the valuable insights based on the previous paper,
there are several gaps identified that motivate this study.
The objective of this study particularly is to gain insights
into adopters’ perspectives. The results of performing
SLR are to understand the recent study on ERS and theexisting gap between previous research and studies.
From the results of performing the SLR on the specified
topic, in Malaysia, there is a smaller number of returned
publications with only 37 papers (see Fig.1), specifically,studies regarding ERS adoption. Previous researchsignifies that ERS contributes within the aspect of theengineers and scientists, however, in real-life problems,
there will be a different situation that might not apply.
Based on the Lens search, the availability of ERS islimited due to ERS being practically known amongst
researchers related to the field of computer science,engineering, artificial intelligence and information
systems. And when we narrowed down the scope to
Malaysia, the trend of publication is slower during theearly year between 2011-2016 due to the technology is
still in a growth state. However, in 2017-2021, thepublications are getting momentum again along with theadvancement of technology, and emotion recognition has
gotten recognizability throughout Malaysia. Next, wedive deep into the adoption towards the Malaysian
population. In 2021, there one paper signifies theacceptance of Malaysian university students towards
ERS [39]. This paper discusses the perspective of
Malaysian university student awareness towards ERS
whether they know about ERS and are ready to adopt thetechnology or they are still unaware of the technology.
In conclusion, there is still a lack of studies related to
ERS in Malaysia and ERS adoption and readiness in
Malaysia. This study will help to gain better insights on
the perspective of ERS adoption and readiness andcontribute to the practitioners and policy making by thegovernment to ensure that ERS is more practical to use
amongst users and important determinants of user’s
adoption in ERS.

199

4.4. Limitations

environment [58]. Therefore, the adoption of ERS in
education could be beneficial for instructors, institutions,and students, and could serve as a step towards moreadvancedhuman-computerinteraction(HCI)
technologies. Such applications of ERS in education canpave the way for future proofing and the development of
more advanced HCI systems that enable personalized
learning experiences. Furthermore, utilization of ERS in
the education sector can facilitate for better adoption
rates of ERS in other sectors as the youths familiarize tothe technology during their formative years.
The implementation of ERS in the future hassignificant potential for the development of advanced
technological possibilities. However, it is crucial toidentify the readiness for ERS adoption, as the successful
integration of new technologies into daily life requires
thorough research to identify the influencing factors.
Therefore, further research is needed to determine thereadiness of societies for ERS adoption and to support
further findings. Such research can inform thedevelopmentofpolicies,
guidelines
andrecommendations for the integration of ERS into various
contexts and can contribute to the overall success of ERS
adoption.
Lastly, ERS engineers and practitioners need to beaware of the importance of ERS readiness, as the
expectations for ERS to work in the real world and be
accepted by societies may differ from their technicalrequirements [40], [59]. Therefore, future researchshould focus on identifying the readiness factors ofsocieties in adopting ERS, which can provide valuable
insights for ERS engineers and practitioners indeveloping ERS systems that align with societal
expectations and values. By considering societal factors
and readiness levels, ERS systems can be tailored to meetthe specific needs of different contexts, which cancontribute to the successful adoption and implementationof ERS.

Conducting the SLR studies, and analyzing theprevious paper, some limitations have been found andhighlighted by the previous researchers. Firstly, thesecurity and privacy from the emotional recognition. As
machines evolve for to better over the next few years,
there is a part where for humans, exposing privacy is thelast part to do. Although in marketing, there is a benefit
of ERS to be implemented, however, there is a risk to
privacy and security, as capturing facial recognition andanalyzing text data mining can feel like invading privacy
and security breach without consent.
Secondly, another study [42], suggested that there is
uncertainty for ERS as a limitation due to recognizing
human emotions can create fear amongst humans.
Specifically, in the workplace, emotion will play animportant role in determining performance, productivity
and efficiency, however, there will be at times whenhumans do not want to be measured for their emotions
and observing their behaviour, therefore, will create
unpleasant surroundings.
Next, from the perspective of social science, there isa limited number of papers from SLR conducted that
focuses on ERS readiness and adoption. There is a needto understand the perspective of users that will be goingto face the technology and the perceived usefulness of thetechnology matters in predicting accurately thetechnology will be performing in real-life.
4.5. Future Directions
The papers reviewed, sourced from Lens, suggest that
there is a need for more research on ERS, as its
applications have the potential to benefit individuals andsocieties in everyday life, particularly in Malaysia.
Furthermore, ERS can play a significant role in industries,
as highlighted by previous researchers, due to the rising
importance of AI development in the fourth industrial
revolution (IR 4.0). As we move towards the fifth
industrial revolution (IR 5.0), with a focus on enhancing
human-computer interaction, the relationship between
ERS and AI is of particular importance. ERS can serve
as a subpart of AI, enabling robots to better understandand interact with humans, and enhancing the overall HCI
experience [57]. Therefore, further research on ERS isnecessary to explore its potential applications and benefit
in various contexts, including education, healthcare, andentertainment, and to pave the way for the developmentof more advanced HCI technologies in the future.
With the evolution of industries towards education,
the use of ERS has the potential to improve theinteraction and feedback in online education [58].
Research has shown that incorporating emotionalrecognition into online learning can enhance the learning
experience by creating a more authentic and engaging

5. Conclusion
With the ubiquity of computer technologies andsmart devices in all aspects of human life, technologicaladvancement is increasingly important in our daily lives.
In this context, ERS can serve as an efficient bridge
towards IR 5.0 innovations and enhance existing
technological applications. ERS technologies leveragevarious modalities such as facial expressions, speech, andphysiological signals to detect and interpret emotional
states, which can provide valuable insights into humanbehaviour and enhance the overall user experience. With
technologies
becoming
more
intelligent
andcollaborative, researchers expected a wider range ofbenefits will materialize for us to experience acrosscommunities as well as generations [10], [57], [60]..
200

Therefore, continued research and development in ERS
technologies is crucial to realize its potential applications
and benefits in various domains and pave the way
towards more advanced and sophisticated technologicalsolutions in the future.

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Acknowledgement
This work was supported by Telekom Malaysia Researchand Development Sdn. Bhd., [Grant Code:
MMUE/190010].
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Mr. Terence Tan Gek Siang
He is a lecturer in the Marketing
Management Department, Faculty ofBusiness, Multimedia University. He
teaches businessandmanagement subjects at bothundergraduate and postgraduatelevel. He is currently the Deputy
Head of International Relations in the
Faculty of Business. His researchinterests are in tourism marketing, user acceptance of
information technology, virtual reality, augmented reality,and business management.

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(2023). A Learning Analytics Model Based on
Expression Recognition and Affective Computing:
Review of Techniques and Survey of Acceptance. In
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International BV. https://doi.org/10.2991/978-946463-012-1_19
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Assoc. Prof. Dr. Nor Azlina Ab. Aziz
she is an associate professor in the
FacultyofEngineeringandTechnology at Multimedia University,
Melaka.
Sheisinterested
in the field of soft computing and its
application in engineering problems.
More specifically, her focus is in thearea of swarm intelligence and nature
inspired optimization algorithm.

Authors Introduction
Mr. Muhammad Nadzree Mohd Yamin
He is a Master of Philosophy(Management)
student
from
Multimedia University. He obtained
his Bachelor of Business Management(Hons) in Management (First Class)from Multimedia University in 2021.

Assoc. Prof. Dr. Kamarulzaman Ab. Aziz
He is currently an associate professor
attheFacultyofBusiness, Multimedia University. His
research interest includes Cluster
Development,
Technology
andInnovation
Management,
EntrepreneurshipandCommercialization.

204