Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature

Abstract

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility.

Full text

Citation: Donisi, L.; Cesarelli, G.;
Pisani, N.; Ponsiglione, A.M.;
Ricciardi, C.; Capodaglio, E.
Wearable Sensors and Artificial
Intelligence for Physical Ergonomics:
A Systematic Review of Literature.
Diagnostics 2022,12, 3048. https://
doi.org/10.3390/diagnostics12123048
Academic Editor: Robert Lemoyne
Received: 14 October 2022
Accepted: 1 December 2022
Published: 5 December 2022
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diagnostics
Systematic Review
Wearable Sensors and Artificial Intelligence for Physical
Ergonomics: A Systematic Review of Literature
Leandro Donisi 1, 2, *,† , Giuseppe Cesarelli 1 ,2 ,† , Noemi Pisani 3 ,* , Alfonso Maria Ponsiglione 4,
Carlo Ricciardi 2,4 and Edda Capodaglio 2
1Department of Chemical, Materials and Production Engineering, University of Naples Federico II,
80125 Naples, Italy
2Istituti Clinici Scientifici ICS Maugeri, 27100 Pavia, Italy
3Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
4Department of Information Technology and Electrical Engineering, University of Naples Federico II,
80125 Naples, Italy
*Correspondence: leandro.donisi@unina.it (L.D.); noemi.pisani@unina.it (N.P.)
† These authors contributed equally to the work.
Abstract:
Physical ergonomics has established itself as a valid strategy for monitoring potential
disorders related, for example, to working activities. Recently, in the field of physical ergonomics,
several studies have also shown potential for improvement in experimental methods of ergonomic
analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard,
this review intends to provide a first account of the investigations carried out using these combined
methods, considering the period up to 2021. The method that combines the information obtained on
the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors
(EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning
or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive
points of view. In particular, the signals, obtained from wearable sensors for the recognition and
categorization of the postural and biomechanical load of the worker, can be processed to formulate
interesting algorithms for applications in the preventive field (especially with respect to muscu-
loskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational
Medicine, these applications improve the knowledge of the limits of the human organism, helping in
the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and
work organization. The growth prospects for this research area are the refinement of the procedures
for the detection and processing of signals; the expansion of the study to assisted working methods
(assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities;
as well as the development of risk assessment systems that exceed those currently used in ergonomics
in precision and agility.
Keywords:
biomechanical risk assessment; deep learning; ergonomics; health monitoring; inertial
measurement unit; machine learning; occupational medicine; physical ergonomics; wearable sensors;
work-related musculoskeletal disorders
1. Introduction
Ergonomics deals with the design of work environments so that they are suitable
for humans, and aims at the objectives of health and safety, and productivity at work [
1
].
Ergonomics as a discipline stands out for its systemic approach, design orientation, and the
joint consideration of human well-being and performance [2].
Physical ergonomics is concerned with human anatomical, anthropometric, phys-
iological and biomechanical characteristics as they relate to physical activity. Relevant
topics include working postures, materials handling, repetitive movements, work-related
musculoskeletal disorders (WMSDs), workplace layout, physical safety, and health [
3
].
Diagnostics 2022,12, 3048. https://doi.org/10.3390/diagnostics12123048 https://www.mdpi.com/journal/diagnostics

Diagnostics 2022,12, 3048 2 of 21
High exposure to physical work is a known risk factor for developing poor health [4] and
sickness absence [
5
], and for the increase in musculoskeletal morbidity [
6
], as well as for
the reduction in working life expectancy [
7
]. A thorough ergonomic assessment is the
foundation for creating a safer, healthier, less injury-prone workplaces, and improving
overall workplace wellness [
8
]. Ergonomists traditionally use various methods of analysis
to determine risk factors per job or task to quantify stressors and prioritize them, in order
to assist in the development of appropriate controls [
9
,
10
]. Specific analysis techniques may
include biomechanical models, energy expenditure evaluations, time and motion studies,
force measurement, postural analysis, and standardized evaluation tools. Collected data
are compared against scientific information and normative data, and interventions in the
workplace are planned to eliminate or control risk factors.
Technological innovation, and in particular wearable devices [
11
,
12
], offer the possi-
bility of objectively and automatically detecting both the physical stress associated with job
requests, and the strain caused on the worker involved. This is carried out independently
of the presence of an external observer or physical instruments applied on the worker, with
minimal invasiveness, and even in complex occupational situations. Moreover, continuous
and context-related measurement through sensors integrates motor behaviours and exe-
cution techniques adopted by the worker, offering the possibility to study these aspects
in their association with efficiency, productivity, and job security [
13
]. The treatment of
data obtained from sensors for a diagnostic, prognostic or preventive purpose [
14
] takes
advantage from the application of Artificial Intelligence (AI) [
15
,
16
]. In particular, Machine
Learning (ML) and Deep Learning (DL) allow us to extract interesting features and to
study them by detecting any associations with the onset of WMSDs [
17
], the occurrence of
injuries [18], or other prognostic factors [19].
In current and foreseen employment contexts, characterized by the complexity of the
work organization, the absence of exactly decodable tasks—as well as the aging of the
workforce, and the emergence of situations with exposure to multiple risk factors [
20
]—it is
of fundamental interest to adopt a holistic vision of the system [worker-activity-environment].
Ideally, the combination of wearable sensors and AI could help ergonomics in identifying
the factors that promote occupational well-being, directing the targeted use of economic
resources to implement ergonomic design that contributes to the primary prevention of
health issues in the workers. Secondly, the use of wearable sensors and AI could help
to verify the long-term tolerability conditions of work, through an accurate recognition
of the exposure conditions, integrating the strain aspects developed by the worker, and
comparing them with the work requirements.
Innovations in AI (sensors, robots, ML algorithms) have been shown to increase
productivity, and could potentially improve the safety and health of workers in the work-
place [
21
]. Therefore, it is very crucial to have a thorough understanding of AI methods,
and of the effects of these methods on the workers and workplaces as well.
As reported by Karwowski [
22
], the conventional domains of ergonomics can be
summarized in three classes:
•
Physical ergonomics related to physical activity concerning human anatomical characteristics;
•Cognitive ergonomics related to mental processes;
•Organizational ergonomics related to optimization of socio-technical systems.
To the best of the authors’ knowledge, no systematic reviews consider the potential
combined use of wearable devices and AI algorithms in physical ergonomics applications.
Some reviews have focused on the potential use of wearable devices in ergonomics [
12
,
23
–
25
],
while others have focused on the role of ML in the prevention of WMSDs [
17
,
26
]. This
systematic review aims to fill this gap in the literature, considering the growing use of
wearable devices and AI in medicine, and particularly in occupational medicine.
2. Research Strategy
The systematic review is a method of selecting, evaluating, and summarizing stud-
ies based on a specific topic [
27
]. Our systematic review is presented according to the

Diagnostics 2022,12, 3048 3 of 21
Preferred Reported Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting
guidelines [28].
Search Methodology and Study Selection
The literature search was conducted on Scopus and PubMed databases, and it was
limited to English documents. Each database was queried using the following keyword
structure: (“wearable” OR “sensors”) AND (“ergonomics” OR “occupational medicine”
OR “occupational health”) AND (“AI” OR “ML”).
In order to simplify our research, the exclusion criteria were:
•Conference reviews, reviews, book chapters and erratum;
•Papers not available;
•Papers duplicated.
Concerning the screening by title, abstract, and full text, the following exclusion
criteria were defined:
•
Papers proposing human-machine interface solutions without wearable devices, and
not explicitly related to occupational medicine (e.g., touchless control interface in an
underwater simulation environment [29]);
•Papers proposing wearable devices for cognitive ergonomics (e.g., [30]);
•Papers proposing only a wearable device solution without AI (e.g., [31]);
•Papers proposing wearable devices for other purposes (e.g., rehabilitation [32]).
Documents were screened evaluating, firstly, title and abstract contents and, in case
the documents did not meet the inclusion criteria, secondly the full text. Figure 1shows
the PRISMA workflow, and the number of documents included in this systematic review.
Diagnostics 2022, 12, x FOR PEER REVIEW 3 of 25
2. Research Strategy
The systematic review is a method of selecting, evaluating, and summarizing studies
based on a specific topic [27]. Our systematic review is presented according to the
Preferred Reported Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting
guidelines [28].
2.1 Search Methodology and Study Selection
The literature search was conducted on Scopus and PubMed databases, and it was
limited to English documents. Each database was queried using the following keyword
structure: (“wearable” OR “sensors”) AND (“ergonomics” OR “occupational medicine”
OR “occupational health”) AND (“AI” OR “ML”).
In order to simplify our research, the exclusion criteria were:
• Conference reviews, reviews, book chapters and erratum;
• Papers not available;
• Papers duplicated.
Concerning the screening by title, abstract, and full text, the following exclusion
criteria were defined:
• Papers proposing human-machine interface solutions without wearable devices, and
not explicitly related to occupational medicine (e.g., touchless control interface in an
underwater simulation environment [29]);
• Papers proposing wearable devices for cognitive ergonomics (e.g., [30]);
• Papers proposing only a wearable device solution without AI (e.g., [31]);
• Papers proposing wearable devices for other purposes (e.g., rehabilitation [32]).
Documents were screened evaluating, firstly, title and abstract contents and, in case
the documents did not meet the inclusion criteria, secondly the full text. Figure 1 shows
the PRISMA workflow, and the number of documents included in this systematic review.
Figure 1. Summary review workflow.
Figure 1. Summary review workflow.
3. Main Findings and Argumentation
The systematic review includes 25 papers divided into journal articles (16 out of 25),
and conference papers (9 out of 25).

Diagnostics 2022,12, 3048 4 of 21
Most of the papers were published between 2018 and 2021, peaking in 2020 as shown
in Figure 2, underlining the growing interest in occupational ergonomics both from a
practical and research point of view.
Diagnostics 2022, 12, x FOR PEER REVIEW 4 of 25
3. Main findings and Argumentation
The systematic review includes 25 papers divided into journal articles (16 out of 25),
and conference papers (9 out of 25).
Most of the papers were published between 2018 and 2021, peaking in 2020 as shown
in Figure 2, underlining the growing interest in occupational ergonomics both from a
practical and research point of view.
Figure 2. Distribution of papers over time.
The papers were analyzed according to several categories: aim of the study; people
involved, and task performed by the subjects; type of wearable device and its positioning
on the human body; signal acquired by the sensor and features extracted; principles,
methods, standards and/or guidelines underlying the ergonomic assessment; AI strategy
(ML and/or DL); results of the studies. Table 1 shows the papers in descending order by
year.
Figure 2. Distribution of papers over time.
The papers were analyzed according to several categories: aim of the study; people
involved, and task performed by the subjects; type of wearable device and its positioning
on the human body; signal acquired by the sensor and features extracted; principles,
methods, standards and/or guidelines underlying the ergonomic assessment; AI strategy
(ML and/or DL); results of the studies. Table 1shows the papers in descending order
by year.

Diagnostics 2022,12, 3048 5 of 21
Table 1. Analysis of the studies included in the review.
Study Scope Population Sensor
(Positioning) Signal
Acquired Task Ergonomic
Criteria
AI Strategy
(Algorithms) Extracted
Features Results
Mudiyanselage et al.
(2021)
[33]
Detecting the level of risk
of harmful lifting
activities characterized
by the NIOSH Lifting
Index using ML models
trained with sEMG
sensor data
1 volunteer healthy
subject
2 wireless sEMG
muscle sensors
(Thoracic and
Multifidus muscles)
EMG signal Lifting loads RNLE
ML and DL
(RF, DT, GB, AB,
KNN, NB, SVM,
LR, MP)
Weight, horizontal
location of the object
relative to the body,
Min, Median, SD
All ML models
showed an accuracy
greater than 98%; the
best algorithm
was DT
(accuracy = 99.96%)
Donisi et al.
(2021)
[34]
Discriminating
biomechanical risk
classes according to the
RNLE using a wearable
inertial sensor and
ML algorithms
7 volunteer healthy
subjects
1 IMU sensor
(Lumbar region) Linear Acceleration,
Angular velocity Lifting loads RNLE
ML and DL
(RF, DT, GB, AB,
KNN, NB, SVM,
LR, MP)
RMS, SD, Min, Max
RF was the best
algorithm for all
evaluations
conducted
(accuracy > 90% and
AUC-ROC > 94%)
Aiello et al.
(2021)
[35]
Classifying heavy-duty
and hard-duty activities
considering the exposure
to vibration by means of
a developed wearable
device and ML classifier
worker healthy
subjects (nns)
2 accelerometers
(Wrists) Linear Acceleration
Rotating tools
(e.g., grinding,
polishing, cutting,
etc.)
ISO 5349-1 (2001a)
ISO 5349-2 (2001b)
ML
(KNN)
Time domain
features
(mean, SD, Max,
Min, RMS, skewness,
kurtosis)
The accuracy of
KNN (k = 3)
classifier was 94%
Zhao & Obonyo
(2021)
[36]
Recognizing workers’
posture using inertial
data and DL
9 worker healthy
subjects
5 IMU sensors
(Forehead, Chest
center, Right upper
arm, Right thigh,
Right calf)
Linear Acceleration,
Angular velocity
Construction
work activities OWAS DL
(CLSTM) NS Macro F1 score was
about 85%
Matijevich et al.
(2021)
[37]
Finding the best
combination of wearable
sensors to monitor low
back loading during
manual material
handling
10 volunteer healthy
subjects
IMU sensors,
Pressure insoles
(Feet, Shanks,
Thighs, Pelvis,
Trunk)
Linear Acceleration,
Angular velocity,
Foot plantar
pressure
Manual material
handling NS ML and DL
(GLMs, SVMs, NNs,
GBDT)
Kinematic and
kinetic features
GBDT algorithm
showed R2= 89%
combining trunk
IMU and pressure
insoles
Campero-Jurado
et al.
(2020)
[38]
Presenting a smart
helmet prototype that
monitors the conditions
in the workers’
environment and
performs a near real-time
evaluation of risk by
means of AI algorithms
1 worker healthy
subject
Light sensor, Shock
sensor,
Accelerometer and
Gyroscope
(Head)
Atmospheric
pressure,
Environment
temperature,
Humidity,
Brightness, Shock
alerts, Linear
Acceleration,
Angular velocity
Generic work
activities JSA ML and DL
(SVM, NB, SNN,
CNN)
Brightness, Variation
in X, Y and Z axis,
Force Sensitive
Resistor,
Temperature,
Humidity, Pressure,
Air quality
SVM showed the
lowest accuracy
(68.51%). NB and
SNN achieved an
accuracy of 78%.
CNN showed the
best accuracy
(92.05%)
Akanmu et al.
(2020)
[39]
Developing a
cyber-physical postural
training architecture that
provides feedback to
perform real construction
tasks in safe postures
10 volunteer healthy
subjects
19 IMU sensors,
Virtual reality
head-mounted
display
(Head, Arms,
Thorax, Waist, Legs)
Linear Acceleration,
Angular velocity,
Image
Construction
work activities PERA ML
(Reinforcement
learning algorithm) Kinematic features
Numerical results
not provided
(color feedback
associated to the risk
level)

Diagnostics 2022,12, 3048 6 of 21
Table 1. Cont.
Study Scope Population Sensor
(Positioning) Signal
Acquired Task Ergonomic
Criteria
AI Strategy
(Algorithms) Extracted
Features Results
Umer et al.
(2020)
[40]
Predicting physical
exertion levels using
multiple physiological
measures
10 volunteer healthy
subjects
ECG sensor, Skin
temperature sensor,
Respiration sensor
(Thorax)
ECG, Skin
temperature,
Respiration
Construction
work activities
(manual material
handling)
Borg-20 scale
ML
(KNNs, SVM, DAs,
DTs, Ensemble
classifiers)
Mean, Max, Min,
Variance, Range, SD,
Kurtosis,
Anthropometric
characteristics,
Activity duration
The ensemble
classifier (bagged
trees) showed the
best accuracy
(95.3%)
Conforti et al.
(2020)
[41]
Recognizing safe and
unsafe postures through
wearable sensors and ML
algorithms fed with
kinematic data
26 volunteer healthy
subjects
8 IMU sensors
(Sternum, Pelvis,
Thighs, Shanks, Feet)
Linear Acceleration,
Angular velocity Manual material
handling NS ML
(SVM) Kinematic features SVM showed an
accuracy of 99.4%
Zhao & Obonyo
(2020)
[42]
Proposing a CLSTM
model for recognizing
construction workers’
postures
4 worker healthy
subjects
5 IMU sensors
(Forehead, Chest
center, Right upper
arm, Right thigh,
Right crus)
Linear Acceleration,
Angular velocity
Construction
work activities OWAS DL
(CLSTM) NS Macro F1 score was
greater than 79%
Antwi-Afari et al.
(2020)
[43]
Recognizing workers’
activities related to
overexertion from data
captured by a wearable
insole pressure system
2 volunteer healthy
subjects
13 capacitive sensors,
Accelerometer
(Feet)
Foot plantar
pressure, Linear
Acceleration
Manual material
handling OSHA ML and DL
(DT, RF, KNN, SVM,
ANN)
Time domain
features (mean,
variance, Max, Min,
range, SD, root
mean, RMS, kurtosis,
skewness, SD
magnitude, sum
vector magnitude,
signal magnitude
area)
Frequency domain
features (spectral
energy, entropy
spectrum)
Spatial-temporal
features
(pressure-time
integral,
anterior/posterior
center of pressure,
medial/lateral
center of pressure)
The best classifier
was RF with an
accuracy over 97%
Asadi et al.
(2020)
[44]
Presenting a computer
vision model that
distinguishes between
two (high and low) and
three
(100% MVC/50% MVC/
0% MVC) force
exertion levels
18 volunteer healthy
subjects
Hand dynamometer,
Pulse oximeter
(Hand)
Grip force, PPG Isometric force
exertions
Moore-Garg Strain
Index
ML and DL
(RF, SVM, KNN,
DNN)
Facial features
(Average and SD),
PPG features
(SD, Rise Time,
Fall Time)
The DNN classifier
showed the best
performance for all
evaluation metrics

Diagnostics 2022,12, 3048 7 of 21
Table 1. Cont.
Study Scope Population Sensor
(Positioning) Signal
Acquired Task Ergonomic
Criteria
AI Strategy
(Algorithms) Extracted
Features Results
Estrada & Vea
(2020)
[45]
Recognizing proper and
improper sitting posture
to the laptop
60 volunteer healthy
subjects
10 flex sensors
(Upper body) Bending Sitting NS ML
(DT)
Gender, Age, Height,
Weight, Wrist size,
Category, Chair
height, Distance,
Bending features
DT showed a
precision of 83.29%
and 78.57% and a
recall of 76.86% and
84.62% for proper
and improper sitting
postures,
respectively. The
accuracy was 80%
Fridolfsson et al.
(2020)
[46]
Classifying work specific
activities captured from a
shoe-based sensor in a
lab setting using ML
models and validating
these models in a
free-living setting
35 volunteer healthy
subjects,
29 worker healthy
subjects
Accelerometers
(Heel-cap) Linear Acceleration
Sitting, Standing,
Walking,
Weight carrying,
Kneeling;
logistics
warehouse and
industrial
production
activities
NS ML
(RF, SVM, KNN)
Mean, SD, Skewness,
Kurtosis, Energy,
Correlation
RF was the best
algorithm for both
classification and
validation model
showing an accuracy
of 96.3% and 71.2%,
respectively
Manjarres et al.
(2020)
[47]
Tracking physical
workload using human
activity recognition and
HR measurements using
wearable devices data
29 volunteer healthy
subjects
Accelerometer, PPG
sensor
(Hip, Wrist)
Linear Acceleration,
PPG
Jogging, Doing
crunches,
Push-ups,
Squatting,
Standing
Firmat’s score ML
(RF, KNN)
Mean, SD, Variance,
Median absolute
deviation
The best results
showed an overall
accuracy of 97.7%
for RF
Zhang et al.
(2019)
[48]
Recognizing jerk changes
due to physical exertion
using jerk-based features
as input to SVM
classifiers
6 worker healthy
subjects
17 IMU sensors
(Pelvis, Sternum,
Head,
Both shoulders,
Upper arms, Lower
arms, Hands, Upper
legs, Lower
legs, Feet)
Linear Acceleration,
Angular velocity Bricklaying
activities NS ML
(SVM)
Mean, SD, Max, Min,
Jerk cost, Dominant
frequency
The SVM classifier
showed an accuracy
over 80%
Low et al.
(2019)
[49]
Classifying workers
movement using ML
algorithm by acquiring
accelerometer data
5 volunteer healthy
subjects
Accelerometer
(Waist, Wrist) Linear Acceleration
Bending full
forward, Bending
midway forward,
Squatting,
Twisting
Rodger Muscle
Fatigue Analysis
ML
(LinR, LR) Accelerometer
features
LR has
outperformed LinR
in classification
tasks, by achieving
an accuracy of 73%
Lim & D’Souza
(2019)
[50]
Examining potential
gender effects for
predicting hand-load
levels using body-worn
inertial sensor data
22 volunteer healthy
subjects
3 inertial sensors
(Thorax, Lumbar,
Shank)
Linear Acceleration,
Angular velocity Carrying a box NS ML
(RF)
Gait features,
Postural sway
features,
Mean relative
phase angles
The classification
accuracy was 74.2%
and 80.0% for men
and women models,
respectively

Diagnostics 2022,12, 3048 8 of 21
Table 1. Cont.
Study Scope Population Sensor
(Positioning) Signal
Acquired Task Ergonomic
Criteria
AI Strategy
(Algorithms) Extracted
Features Results
Xie & Chang
(2019)
[51]
Proposing a wearable
safety assurance system
framework for power
operation to improve the
capacity of emergency
control over on-site
operation risk and
guarantee safety of
operators in a
complicated environment
1 worker healthy
subject
Gyroscope sensor,
Electro-cardio sensor,
Pulse sensor, 9 IMU
sensors, Body
temperature sensor,
PPG sensor
(Wrist, Arm)
Linear Acceleration,
Angular velocity,
ECG, Pulse, Body
temperature, Blood
oxygen, Blood
pressure, HR,
Breathing rate, PPG
Routine work
tasks (electric
substation)
NS ML
(SVM)
Time domain
features
(HR, SDANN)
Frequency domain
features (Very low
frequency, Low
frequency, High
frequency)
Multi-scale entropy
features (Sample
entropy)
NS numeric results
Martire et al.
(2018)
[52]
Detecting the presence of
a digital screen in front of
the user in different
environments through a
color light sensor placed
on the head during
daily activities
5 healthy subjects
(ns volunteer
or worker)
1 color light sensor
(Forehead) Brightness
Office activities
(read documents
or papers,
simulate a
lesson etc.)
NS ML
(RF, NB) NS
The overall accuracy
obtained was 79.3%
for RF and 70.1%
for NB
Antwi-Afari et al.
(2018)
[53]
Detecting and classifying
awkward working
postures using AI models
trained with foot plantar
pressure distribution data
10 volunteer healthy
subjects
13 capacitive sensors,
Accelerometer
(Feet)
Foot plantar
pressure, Linear
Acceleration
Construction
work activities ISO 11226:2000 ML and DL
DT, KNN, SVM,
ANN)
Time domain
features (Mean
pressure, Variance,
Max pressure, Min
pressure, Range, SD,
Kurtosis)
Frequency domain
features (Spectral
energy, Entropy)
Spatial temporal
(Pressure time
integral)
The SVM classifier
showed the best
accuracy (99.90%)
followed by the
KNN (98.70%), DT
(98.40%), and ANN
(98.20%)
Nath et al.
(2018)
[54]
Identifying tree different
classes of worker
activities (push/pull,
lift/lower/carry and no
risk activities) using SVM
classifier and
sensors data
2 worker healthy
subjects
IMU sensors
(Upper arm, Waist) Linear Acceleration,
Angular velocity
Warehouse
operations (lift,
lower, carry,
push, pull)
OSHA ML
(SVM)
Statistical features
(Mean, Min, Max,
SD, Interquartile
range, Skewness,
Kurtosis, Mean
absolute deviation,
4th-order
autoregressive
coefficients)
Accelerometer and
gyroscope features
All activities were
recognized with an
accuracy greater
than 80%
Yu et al.
(2018)
[55]
Calculating workload
and plan ergonomic risks‘
mitigation strategies
using computer vision,
IMU sensors and
pressure insoles
worker healthy
subjects (nns)
Pressure sensors,
IMU sensors
(Feet, Total body)
Foot plantar
pressure, Linear
Acceleration,
Angular velocity
Material handling,
Rebar, Plastering NS DL
(NS) NS NS numeric results

Diagnostics 2022,12, 3048 9 of 21
Table 1. Cont.
Study Scope Population Sensor
(Positioning) Signal
Acquired Task Ergonomic
Criteria
AI Strategy
(Algorithms) Extracted
Features Results
Raso et al.
(2018)
[56]
Providing feedback about
the criticality of the
ergonomic posture in
real-time from pressure
and strain sensor data
according to EAWS
15 worker healthy
subjects
Strain sensors,
Pressure
sensors(Upper body
(Trunk and arms))
Deformation
Pressure Lifting loads,
Drive, Sitting EAWS ML
(ns) NS NS numeric results
Olsen et al.
(2009)
[57]
Classifying correct and
incorrect postures using
ML techniques to
improve the ergonomics
of dental practitioners
11 healthy subjects
(ns volunteer or
worker)
3 inclinometers
(Shoulder blades,
Lower back) Angles
Routine work
tasks (leaning left,
leaning right,
leaning forwards
and backwards,
and slouching)
NS ML and DL
(AB, SVM, LVQ,
KNN, ANN)
Inclinometers
features from x and
y axes
The best performing
algorithm was KNN
which achieves an
accuracy of 99,94%
Abbreviations: AB = AdaBoost, AI = Artificial Intelligence, ANN = Artificial Neural Network, AUC-ROC = Area Under the Receiver Operating Characteristic Curve,
CLSTM = Convolutional Long Short-Term Memory, CNN = Convolutional Neural Network, DA = Discriminant Analysis, DL = Deep Learning, DNN = Deep Neural Network,
DT = Decision Tree
, EAWS = Ergonomic Assessment Worksheet, ECG = Electrocardiography, GB = Gradient Boost, GBDT = Gradient Boost Decision Tree, GLMs = Generalized
Linear Models, HR = Heart Rate, IMU = Inertial Measurement Unit, ISO = International Organization for Standardization, JSA = Job Safety Analysis, KNN = K-Nearest Neighbor,
LinR = Linear Regression
, LR = Logistic Regression, LVQ = Learning Vector Quantization, Max = Maximum, Min = Minimum, ML = Machine Learning, MP = Multilayer Perceptron,
MVC = Maximum Voluntary Contraction, NB = Naive Bayes, NN = Neural Network, nns = number not specified, NS = Not Specified, OSHA = Occupational Safety and Health
Administration,
OWAS = Ovako Working posture Analyzing System
, PERA = Postural Ergonomic Risk Assessment, PPG = Photoplethysmography, RF = Random Forest, RMS = Root
Mean Square, RNLE = Revised NIOSH Lifting Equation, SD = Standard Deviation, sEMG = surface Electromyography, SNN = Static Neural Network, SVM = Support Vector Machine.

Diagnostics 2022,12, 3048 10 of 21
3.1. Wearable Device Type and Study Population
Wearable devices have developed exponentially through novel sensors and technolo-
gies, and the long-term monitoring of vital signs and other principles, as described in [
58
].
The versatility of these devices makes them useful in multiple healthcare scenarios for
several purposes (e.g., chronic diseases, mental health and medical conditions) [
59
–
65
].
Authors included studies on wearable solutions for ergonomic risk to prevent WMSDs and
suggested two device types: prototype and commercial. Prototype device stands for wear-
able devices or a system of wearable devices in a configuration not commercially available,
while commercial device means commercially available solutions. These included studies
recruited healthy subjects, differentiating between volunteers and workers.
Of these studies, 3 out of 25 articles tested a prototype device on healthy volunteer
subjects. Akanmu et al. [
39
] developed an architecture that provides feedback to perform
real construction tasks in safe postures. Manjarres et al. [
47
] suggested a configuration,
composed of human activity recognition hardware and a smartwatch, to track physical
workload. Low et al. [
49
] designed a real-time ergonomic risk assessment system to detect
workers’ movements.
Other prototypes were tested on healthy worker subjects. Specifically, Aiello et al. [
35
]
developed a smart wearable device, placed on wrists, to evaluate vibration risks in industry
context, while Campero-Jurado et al. [
38
] presented a smart helmet to monitor accidents
in a work team; finally, Xie and Chang [
51
] proposed a wearable safety assurance system
framework for workers’ health in complicated environments. For this last contribution, we
showed the system framework in Figure 3.
Diagnostics 2022, 12, x FOR PEER REVIEW 14 of 25
3.1. Wearable Device Type and Study Population
Wearable devices have developed exponentially through novel sensors and
technologies, and the long-term monitoring of vital signs and other principles, as
described in [58]. The versatility of these devices makes them useful in multiple healthcare
scenarios for several purposes (e.g., chronic diseases, mental health and medical
conditions) [59–65]. Authors included studies on wearable solutions for ergonomic risk to
prevent WMSDs and suggested two device types: prototype and commercial. Prototype
device stands for wearable devices or a system of wearable devices in a configuration not
commercially available, while commercial device means commercially available
solutions. These included studies recruited healthy subjects, differentiating between
volunteers and workers.
Of these studies, 3 out of 25 articles tested a prototype device on healthy volunteer
subjects. Akanmu et al. [39] developed an architecture that provides feedback to perform
real construction tasks in safe postures. Manjarres et al. [47] suggested a configuration,
composed of human activity recognition hardware and a smartwatch, to track physical
workload. Low et al. [49] designed a real-time ergonomic risk assessment system to detect
workers’ movements.
Other prototypes were tested on healthy worker subjects. Specifically, Aiello et al.
[35] developed a smart wearable device, placed on wrists, to evaluate vibration risks in
industry context, while Campero-Jurado et al. [38] presented a smart helmet to monitor
accidents in a work team; finally, Xie and Chang [51] proposed a wearable safety
assurance system framework for workers’ health in complicated environments. For this
last contribution, we showed the system framework in Figure 3.
Figure 3. Safety assurance system framework. Reproduced with permission from [51] published by
Springer Nature, 2019.
Some authors tested commercial devices on healthy volunteer subjects. A potential
approach is described in [34]; the authors used the Opal (APDM, Inc, USA) [34], which is
a wearable inertial system for motion capture composed of several Opal sensors
constituted by Inertial Measurement Units (IMUs) [66,67]. Opal sensors communicate
through Bluetooth, with a laptop equipped by Mobility Lab Software thanks to the Access
Point, while the Docking Station charges and configures sensors. Figure 4 shows the Opal
System and the placement of the Opal sensor in the lumbosacral region for the work of
Donisi et al. [34].
Figure 3. Safety assurance system framework. Reproduced with permission from [51] published by
Springer Nature, 2019.
Some authors tested commercial devices on healthy volunteer subjects. A potential
approach is described in [
34
]; the authors used the Opal (APDM, Inc, USA) [
34
], which is a
wearable inertial system for motion capture composed of several Opal sensors constituted
by Inertial Measurement Units (IMUs) [
66
,
67
]. Opal sensors communicate through Blue-
tooth, with a laptop equipped by Mobility Lab Software thanks to the Access Point, while
the Docking Station charges and configures sensors. Figure 4shows the Opal System and
the placement of the Opal sensor in the lumbosacral region for the work of Donisi et al. [
34
].

Diagnostics 2022,12, 3048 11 of 21
Diagnostics 2022, 12, x FOR PEER REVIEW 15 of 25
Figure 4. (a) Opal System; (b) Opal sensor positioning. Reproduced with permission from [34];
published by Multidisciplinary Digital Publishing Institute, 2021.
The Equivital EQ02 Life Monitor system consists of a multi-parameter body worn
sensor [40]. Other examples are the Lafayette Hydraulic Hand Dynamometer, a hand
dynamometer [44], and the AMS AS7264A, namely a tri-stimulus light color sensor [52].
Finally, Fridolfsson et al. [46] used a commercial shoe-based sensor for classifying
work activities on both volunteer, and worker healthy subjects.
3.2. Sensor Type and Positioning
The majority of the studies (18 out of 25) used inertial wearable sensors. Inertial
sensors refer to accelerometers, gyroscopes and magnetometers that measure linear
acceleration, angular velocity and magnetic fields. Typically, three orthogonal
gyroscopes, three orthogonal accelerometers, and three orthogonal magnetometers are
contained in an IMU [68]. Eight studies combine inertial sensors and other sensor types,
as detailed in Table 2.
Table 2. Inertial sensors and complementary wearable sensors’ distributions.
Study Inertial Sensor Complementary Wearable Sensor
Aiello et al. [35] ✓ -
Akanmu et al. [39] ✓ Virtual reality display
Antwi-Afari et al. [43,53] ✓ Capacitive sensors
Campero-Jurado et al. [38] ✓ Light sensor, shock sensor
Conforti et al. [41] ✓ -
Donisi et al. [34] ✓ -
Fridolfsson et al. [46] ✓ -
Lim & D’Souza [50] ✓ -
Low et al. [49] ✓ -
Manjarres et al. [47] ✓ PPG sensor
Matijevich et al. [37] ✓ Pressure insoles
Nath et al. [54] ✓ -
Xie & Chang [51]  Electro-cardio sensor, Pulse sensor,
Body temperature sensor, PPG sensor
Yu et al. [55] ✓ Pressure sensors
Zhang et al. [48] ✓ -
Zhao & Obonyo [36,42] ✓ -
Abbreviations: PPG = Photopletismography.
Figure 4.
(
a
) Opal System; (
b
) Opal sensor positioning. Reproduced with permission from [
34
];
published by Multidisciplinary Digital Publishing Institute, 2021.
The Equivital EQ02 Life Monitor system consists of a multi-parameter body worn
sensor [
40
]. Other examples are the Lafayette Hydraulic Hand Dynamometer, a hand
dynamometer [44], and the AMS AS7264A, namely a tri-stimulus light color sensor [52].
Finally, Fridolfsson et al. [
46
] used a commercial shoe-based sensor for classifying
work activities on both volunteer, and worker healthy subjects.
3.2. Sensor Type and Positioning
The majority of the studies (18 out of 25) used inertial wearable sensors. Inertial
sensors refer to accelerometers, gyroscopes and magnetometers that measure linear ac-
celeration, angular velocity and magnetic fields. Typically, three orthogonal gyroscopes,
three orthogonal accelerometers, and three orthogonal magnetometers are contained in
an IMU [
68
]. Eight studies combine inertial sensors and other sensor types, as detailed in
Table 2.
Table 2. Inertial sensors and complementary wearable sensors’ distributions.
Study Inertial Sensor Complementary Wearable Sensor
Aiello et al. [35]3-
Akanmu et al. [39]3Virtual reality display
Antwi-Afari et al. [43,53]3Capacitive sensors
Campero-Jurado et al. [38]3Light sensor, shock sensor
Conforti et al. [41]3-
Donisi et al. [34]3-
Fridolfsson et al. [46]3-
Lim & D’Souza [50]3-
Low et al. [49]3-
Manjarres et al. [47]3PPG sensor
Matijevich et al. [37]3Pressure insoles
Nath et al. [54]3-
Xie & Chang [51]3Electro-cardio sensor, Pulse sensor,
Body temperature sensor, PPG sensor
Yu et al. [55]3Pressure sensors
Zhang et al. [48]3-
Zhao & Obonyo [36,42]3-
Abbreviations: PPG = Photopletismography.

Diagnostics 2022,12, 3048 12 of 21
In particular, Matijevich et al. [
37
] found the best combination of wearable sensors
to monitor low back loading during manual material handling, using trunk IMU and
pressure insoles.
One of the applications of inertial sensors is the recognition of human postures in
several environments, i.e., during activities in the workplace [
69
]. Posture recognition
also depends on where the sensors are attached to anatomical segments of the human
body. We divided anatomical segments into three categories to show the body parts mostly
considered for the attachment of sensors: “upper body” including the lumbar region,
wrist, head, thorax, arm, sternum, pelvis, hip, shoulders, waist and hand; “lower body”
including the thigh, shank, calf, foot, leg; and “total body” including both the “upper body”
and “lower body”. Figure 5represents inertial sensor distribution according to the three
categories proposed.
Diagnostics 2022, 12, x FOR PEER REVIEW 16 of 25
In particular, Matijevich et al. [37] found the best combination of wearable sensors to
monitor low back loading during manual material handling, using trunk IMU and
pressure insoles.
One of the applications of inertial sensors is the recognition of human postures in
several environments, i.e., during activities in the workplace [69]. Posture recognition also
depends on where the sensors are attached to anatomical segments of the human body.
We divided anatomical segments into three categories to show the body parts mostly
considered for the attachment of sensors: “upper body” including the lumbar region,
wrist, head, thorax, arm, sternum, pelvis, hip, shoulders, waist and hand; “lower body”
including the thigh, shank, calf, foot, leg; and “total body” including both the “upper
body” and “lower body”. Figure 5 represents inertial sensor distribution according to the
three categories proposed.
Figure 5. Distributions of wearable sensors on anatomical segments of human body.
The diagram in Figure 5 shows that most studies placed inertial sensors on the whole
body for posture recognition. Three studies [43,46,53] used accelerometers on the lower
body only, specifically on feet.
Furthermore, a substantial minority of articles used only biopotential sensors,
namely devices that convert a biological response in an electrical signal. In the current
study, examples of biopotential wearable sensors were used by Mudiyanselage et al. [33]
and Umer et al. [40]. Both authors placed wearable sensors on the upper body, precisely
on the thorax, albeit aiming at two different objectives. Specifically, Mudiyanselage et al.
[33] studied the level of risk during lifting activities by means of statistical features of an
electromyographic signal, as well as Umer et al. [40] that predicted physical exertion levels
using statistical features extracted from an electrocardiographic signal. Figure 6 shows the
sensors’ positioning [40].
Figure 6. Sensors’ positioning. Reproduced with permission from [40] published by Elsevier, 2020.
Figure 5. Distributions of wearable sensors on anatomical segments of human body.
The diagram in Figure 5shows that most studies placed inertial sensors on the whole
body for posture recognition. Three studies [
43
,
46
,
53
] used accelerometers on the lower
body only, specifically on feet.
Furthermore, a substantial minority of articles used only biopotential sensors, namely
devices that convert a biological response in an electrical signal. In the current study,
examples of biopotential wearable sensors were used by Mudiyanselage et al. [
33
] and
Umer et al. [
40
]. Both authors placed wearable sensors on the upper body, precisely on
the thorax, albeit aiming at two different objectives. Specifically, Mudiyanselage et al. [
33
]
studied the level of risk during lifting activities by means of statistical features of an
electromyographic signal, as well as Umer et al. [
40
] that predicted physical exertion levels
using statistical features extracted from an electrocardiographic signal. Figure 6shows the
sensors’ positioning [40].
Diagnostics 2022, 12, x FOR PEER REVIEW 16 of 25
In particular, Matijevich et al. [37] found the best combination of wearable sensors to
monitor low back loading during manual material handling, using trunk IMU and
pressure insoles.
One of the applications of inertial sensors is the recognition of human postures in
several environments, i.e., during activities in the workplace [69]. Posture recognition also
depends on where the sensors are attached to anatomical segments of the human body.
We divided anatomical segments into three categories to show the body parts mostly
considered for the attachment of sensors: “upper body” including the lumbar region,
wrist, head, thorax, arm, sternum, pelvis, hip, shoulders, waist and hand; “lower body”
including the thigh, shank, calf, foot, leg; and “total body” including both the “upper
body” and “lower body”. Figure 5 represents inertial sensor distribution according to the
three categories proposed.
Figure 5. Distributions of wearable sensors on anatomical segments of human body.
The diagram in Figure 5 shows that most studies placed inertial sensors on the whole
body for posture recognition. Three studies [43,46,53] used accelerometers on the lower
body only, specifically on feet.
Furthermore, a substantial minority of articles used only biopotential sensors,
namely devices that convert a biological response in an electrical signal. In the current
study, examples of biopotential wearable sensors were used by Mudiyanselage et al. [33]
and Umer et al. [40]. Both authors placed wearable sensors on the upper body, precisely
on the thorax, albeit aiming at two different objectives. Specifically, Mudiyanselage et al.
[33] studied the level of risk during lifting activities by means of statistical features of an
electromyographic signal, as well as Umer et al. [40] that predicted physical exertion levels
using statistical features extracted from an electrocardiographic signal. Figure 6 shows the
sensors’ positioning [40].
Figure 6. Sensors’ positioning. Reproduced with permission from [40] published by Elsevier, 2020.
Figure 6. Sensors’ positioning. Reproduced with permission from [40] published by Elsevier, 2020.

Diagnostics 2022,12, 3048 13 of 21
Finally, other studies [
44
,
45
,
52
,
56
,
57
] proposed different sensors, such as: skin temper-
ature sensors, respiration sensors, hand dynamometers, pulse oximeters, flex sensors, color
light sensors, capacitive sensors, strain sensors, pressure sensors, and inclinometers.
3.3. Ergonomic Criteria
Manual material handling is an important risk factor for the development of WMSDs
in construction workers. Ergonomic criteria allow the quantification of risk levels during
manual handling activities [
70
], such as those used to design the tasks depicted in Figure 7.
Diagnostics 2022, 12, x FOR PEER REVIEW 17 of 25
Finally, other studies [44,45,52,56,57] proposed different sensors, such as: skin
temperature sensors, respiration sensors, hand dynamometers, pulse oximeters, flex
sensors, color light sensors, capacitive sensors, strain sensors, pressure sensors, and
inclinometers.
3.3. Ergonomic Criteria
Manual material handling is an important risk factor for the development of WMSDs
in construction workers. Ergonomic criteria allow the quantification of risk levels during
manual handling activities [70], such as those used to design the tasks depicted in Figure
7.
One of the ergonomic criteria quoted in the systematic literature review is the
Revised NIOSH Lifting Equation (RNLE). The RNLE is a manual material handling risk
assessment method associated with lifting and lowering tasks in the workplace [71,72].
Mudiyanselage et al. [33] determined three risk classes (“Normal Risk”, “Increased Risk”
and “High Risk”) according to the Revised NIOSH Lifting Equation. All the variables
(included in the RNLE) were used to calculate the Recommended Weight Load, and the
related Lifting Index (LI) values ranging from 0.8 to 3.2. Similarly, Donisi et al. [34]
introduced two risk classes (“No Risk” and “Risk”) by combining height, frequency, and
weight variables of lifting tasks. They computed two LI values equal to 0.5 and 1.3. Lifting
phases of the lifting task are illustrated in Figure 7a, where subjects performed lifting
activities using a plastic container with weight equally distributed.
Figure 7. Lifting phases of the lifting task. (a) Reproduced with permission from [34]; published by
Multidisciplinary Digital Publishing Institute, 2021. (b) Reproduced with permission from [54]
published by Elsevier, 2018.
Figure 7.
Lifting phases of the lifting task. (
a
) Reproduced with permission from [
34
]; published
by Multidisciplinary Digital Publishing Institute, 2021. (
b
) Reproduced with permission from [
54
]
published by Elsevier, 2018.
One of the ergonomic criteria quoted in the systematic literature review is the Revised
NIOSH Lifting Equation (RNLE). The RNLE is a manual material handling risk assessment
method associated with lifting and lowering tasks in the workplace [
71
,
72
]. Mudiyanselage
et al. [
33
] determined three risk classes (“Normal Risk”, “Increased Risk” and “High Risk”)
according to the Revised NIOSH Lifting Equation. All the variables (included in the RNLE)
were used to calculate the Recommended Weight Load, and the related Lifting Index (LI)
values ranging from 0.8 to 3.2. Similarly, Donisi et al. [
34
] introduced two risk classes (“No
Risk” and “Risk”) by combining height, frequency, and weight variables of lifting tasks.
They computed two LI values equal to 0.5 and 1.3. Lifting phases of the lifting task are
illustrated in Figure 7a, where subjects performed lifting activities using a plastic container
with weight equally distributed.
Two papers [
36
,
42
] classified sensor-detected postures of construction workers, using
the Ovako Work Posture Analysis System (OWAS) as a reference. The OWAS method
identifies safe/unsafe posture that causes WMSDs [73].
Another ergonomic criterion found in the review, and which was used to prevent
ergonomic risk factors, is the Occupational Safety and Health Administration (OSHA) [
74
].

Diagnostics 2022,12, 3048 14 of 21
On one side, Antwi-Afari et al. [
43
] estimated the ergonomic risk levels (“Low”, “Moderate”
and “High”) according to OSHA by means of the weight of the object, while on the other
side Nath et al. [
54
] estimated the same ergonomic risk levels through the duration and
frequency of pushing/pulling, and carrying/lowering/lifting activities. These activities
are illustrated in Figure 7b.
A total of 9 out of 25 studies used different principles, methods, standards, and/or
guidelines for ergonomic risk, in particular: International Organization for Standardization
(ISO) 5349 and 11226 [
35
,
53
], Job Safety Analysis (JSA) [
38
], Postural Ergonomic Risk As-
sessment (PERA) [
39
], Borg-20 scale [
40
], Moore-Garg Strain Index [
44
], Firmat’s score [
47
],
Rodger Muscle Fatigue Analysis [49], Ergonomic Assessment Worksheet (EAWS) [56].
Finally, 10 out of 25 articles did not mention a specific ergonomic criterion. For instance,
Estrada and Vea [
45
] classified the sitting posture as ergonomically correct and incorrect,
by means of flexible wireless sensors connected to a server. Martire et al. [
52
] evaluated
the ability of AI algorithms to recognize when a user is looking at a digital screen, with a
binary classification using features extracted from the sensor. Olsen et al. [
57
] measured the
range of postures of the user, by classifying them as ergonomically correct and incorrect
using inclinometers placed on the laboratory coat.
3.4. Artificial Intelligence Strategy
ML and DL are two branches of AI that can help to prevent WMSDs, as studied in [
17
].
In the included articles, the distribution of the methodologies adopted is: 3 studies applied
DL, 14 studies applied ML, and 8 studies applied both ML and DL. The most frequently
employed algorithms were ensemble classifiers, followed by Support Vector Machines
(SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN), Decision Trees
(DTs), generalized linear models, and Naïve Bayes (NB) classifiers. Figure 8shows the
occurrences of the AI algorithms.
Diagnostics 2022, 12, x FOR PEER REVIEW 18 of 25
Two papers [36,42] classified sensor-detected postures of construction workers, using
the Ovako Work Posture Analysis System (OWAS) as a reference. The OWAS method
identifies safe/unsafe posture that causes WMSDs [73].
Another ergonomic criterion found in the review, and which was used to prevent
ergonomic risk factors, is the Occupational Safety and Health Administration (OSHA)
[74]. On one side, Antwi-Afari et al. [43] estimated the ergonomic risk levels (“Low”,
“Moderate” and “High”) according to OSHA by means of the weight of the object, while
on the other side Nath et al. [54] estimated the same ergonomic risk levels through the
duration and frequency of pushing/pulling, and carrying/lowering/lifting activities. These
activities are illustrated in Figure 7b.
A total of 9 out of 25 studies used different principles, methods, standards, and/or
guidelines for ergonomic risk, in particular: International Organization for
Standardization (ISO) 5349 and 11226 [35,53], Job Safety Analysis (JSA) [38], Postural
Ergonomic Risk Assessment (PERA) [39], Borg-20 scale [40], Moore-Garg Strain Index
[44], Firmat’s score [47], Rodger Muscle Fatigue Analysis [49], Ergonomic Assessment
Worksheet (EAWS) [56].
Finally, 10 out of 25 articles did not mention a specific ergonomic criterion. For
instance, Estrada and Vea [45] classified the sitting posture as ergonomically correct and
incorrect, by means of flexible wireless sensors connected to a server. Martire et al. [52]
evaluated the ability of AI algorithms to recognize when a user is looking at a digital
screen, with a binary classification using features extracted from the sensor. Olsen et al.
[57] measured the range of postures of the user, by classifying them as ergonomically
correct and incorrect using inclinometers placed on the laboratory coat.
3.4. Artificial Intelligence Strategy
ML and DL are two branches of AI that can help to prevent WMSDs, as studied in
[17]. In the included articles, the distribution of the methodologies adopted is: 3 studies
applied DL, 14 studies applied ML, and 8 studies applied both ML and DL. The most
frequently employed algorithms were ensemble classifiers, followed by Support Vector
Machines (SVM), Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN),
Decision Trees (DTs), generalized linear models, and Naïve Bayes (NB) classifiers. Figure
8 shows the occurrences of the AI algorithms.
Figure 8. Employed algorithm distributions.
Ensemble classifiers combine a set of several ML algorithms, named base learners, to
obtain a single classifier that outperforms the others [75]. In the included studies,
Figure 8. Employed algorithm distributions.
Ensemble classifiers combine a set of several ML algorithms, named base learners, to
obtain a single classifier that outperforms the others [
75
]. In the included studies, ensemble
classifiers consist of Random Forest (RF), AdaBoost (AB), Gradient Boost (GB), and Gradient
Boost Decision Trees (GBDTs). SVM classifier is a ML technique that creates a gap between
the classes, maximizing the distance between them, and reducing misclassification error [
76
].
ANNs consist of input and output elements called artificial neurons that try to reproduce
synaptic links by improving results of conventional algorithms. The output neurons are a
weighted sum of input ones [
77
]. In the present work, ANNs include Multilayer Perceptron
(MP), Convolutional Long Short-Term Memory (CLSTM), Static Neural Network (SNN),

Diagnostics 2022,12, 3048 15 of 21
Convolutional Neural Network (CNN), and Learning Vector Quantization (LVQ). The kNN
algorithm is able to make a good classification of an instance if its k-nearest neighbors
have the same label. The classification is based on Euclidean distance [
78
]. DTs represent a
sequential structure that divides the data repeatedly, and can be used for the description,
generalization and classification of data [
79
]. The Generalized Linear Models (GLMs)
provide a generalization of the linear regression by allowing the linear model to be related
to the response variable through a link function [
80
]. GLMs include Linear and Logistic
regression in the systematic review. NB is a probabilistic classifier of supervised learning,
based on Bayes’ theorem. NB classifiers assume that the value of each feature is independent
of the value of any other feature [81].
In terms of accuracy, several classifiers showed high values. Among the ensemble
classifiers, RF was the best algorithm showing accuracy values above 90%. Antwi-Afari
et al. [
43
] achieved an accuracy value of 97% in recognizing activities related to overex-
tension, while Manjarres et al. [
47
] obtained an accuracy value of 97.7% in determining
activities performed by volunteer subjects. The best results for ANNs in the classification of
awkward positions of workers in terms of accuracy (98.20%) were reached by Antwi-Afari
et al. [
53
]. Another strong result is obtained from Campero-Jurado et al. [
38
] detecting
occupational risks by means of CNN and reaching an accuracy of 92.05 %. With the same
number of inertial sensors and an increase of subjects (from 4 to 9), Zhao and Obonyo [
36
,
42
]
improved the results in terms of Macro F1 score, from 79% to 84%, using CLSTM in the
recognition of workers’ postures. Accuracy values over 99% were reached by Conforti
et al. [
41
] (99.4%) using the SVM algorithm fed with kinematic data to recognize safe and
unsafe postures, and by Olsen et al. [
57
] (99.94%) to classify ergonomically correct and
incorrect postures by means of KNN algorithm.
3.5. Feature Extraction
Feature extraction is a useful process of dimensionality reduction and/or redundant
data reduction that avoids the loss of important information. The extracted features
refer to the signals acquired from sensors placed on a specific body part [
82
]. These
features train ML to classify workers’ postures, or to recognize the motor patterns linked to
workers’ activities.
Two studies [
39
,
41
] out of twenty-five used kinematic features extracted from linear
acceleration and angular velocity signals as inputs for two different types of techniques, re-
inforcement learning and supervised learning, respectively. In particular, Conforti et al. [
41
]
used kinematic features (not specified) as SVM inputs that recognized ergonomically
correct, and incorrect, with an accuracy value of 99.4%.
Sensor features could be analyzed to identify an ideal wearable system [
83
]. Matijevich
et al. [
37
] trained several algorithms by means of kinetic and kinematic features in order
to find a combination of wearable sensors to monitor low back biomechanical load. The
authors used two different sets of wearable sensor signals (idealized wearable sensor
signals and real wearable sensor signals) to train ML algorithms. In the ideal configuration,
the algorithm identifies the signals that best estimate the lumbar load, i.e., sagittal trunk
angle, and vertical ground reaction forces; the real configurations confirm the results of the
ideal wearable sensors’ signals.
In some articles [
34
,
35
,
43
,
46
,
47
,
53
,
54
,
80
,
82
] statistical features (time and frequency
domains features) and spatial–temporal features were extracted from inertial sensor signals.
In particular, Donisi et al. [
34
] extracted time-domain statistical features. After computing
feature importance, the authors observed that features associated with acceleration along
the y-axis (i.e., mediolateral direction) are more informative to discriminate between two
risk conditions, according to the RNLE. The ML results, in particular the RF algorithm,
showed an accuracy over 90%. Similarly, Manjarres et al. [47] extracted statistical features
(i.e., mean, standard deviation, variance, median absolute deviation) from the linear accel-
eration signal. The most informative features for RF classifier to track physical workload
were the mean of the z-axis (i.e., perpendicular direction to the sensor plane), besides the

Diagnostics 2022,12, 3048 16 of 21
mean, the standard deviation, and the variance of the x-axis (i.e., vertical direction). In
terms of accuracy, RF showed 97.7%.
Differently from the above-mentioned articles, Mudiyanselage et al. [
33
] extracted
statistical features from the surface electromyographic signal. These features trained ML
algorithms to classify the risk level of harmful lifting activities with an accuracy of 98%.
4. Conclusions
The ergonomic analysis technique that makes use of sensors and AI is mainly aimed at
the prevention of WMSDs, and particularly affects the body sectors of the upper limbs and
back, widely treated in ergonomics. Through this approach, aspects related to the posture of
the whole body have also been partly explored, addressed in ergonomics only recently, and
for which, in the literature, there are still no clear thresholds of sustainability or indications
of optimal levels of variability over time. The application of this approach provides useful
information on the needs of ergonomics to improve the conditions of safety at work, and
the comfort of the worker; to design suitable work environments and equipment; or to set
up work organizations that avoid the onset of phenomena of accumulation of fatigue or
overload. Above all, this approach can be advantageous for the analysis of complex or
difficult to observe work situations.
As the diffusion of this approach progresses, the wealth of knowledge could help
improve the prevention of WMSDs, both associated with acute and cumulative load. This
could provide useful information for setting up working methods that are well tolerated,
even during the entire working life—an important aspect especially for professions with
high biomechanical wear, such as for construction operators or healthcare professionals.
This approach assists not only in the study of the characteristics of force, repetitiveness,
and posture (classic risk factors in physical ergonomics), but also in the kinematic traits
of the worker’s behavior. Specific kinematic traits could be useful as indicators to control
and predict the appearance of any alterations capable of endangering the integrity of the
worker, but also to monitor the critical phases during the return to work for people with
dysfunctions, disabilities or previous pathologies.
Furthermore, the data detectable through sensors can enrich the value of the ergonomic
intervention of evaluation and design, attracting interest also on aspects properly investi-
gated by other disciplines, such as engineering, psychological, organizational, medical, but
also economic ones. The technological approach can be all the more innovative the more
it uses prototypes (rather than commercial standard tools), often made with open-source
resources, and not pre-deterministically channeled towards a single aspect of interest. Con-
sidering some variables detectable through sensors, the design of optimal work situations
can be addressed to specific categories of workers, such as the elderly, in order to be able
to implement targeted adaptations of the workplace that guarantee the expected levels of
productivity and safety.
In addition to the purposes of monitoring, evaluation, and design, the combined
technique that uses sensors and AI opens up new scenarios for ergonomic interventions
of an educational and participatory prevention type; this provides a contribution for
workers to explore new ways of carrying out work, possibly also with the adoption of
technological aids and devices, such as exoskeletons. The illustrated approach also opens
the way to analysis and consideration of multiple conditions of exposure to physical,
chemical, environmental, organizational factors at work, for which neither consolidated
methodologies for risk assessment are currently available nor is evidence of association
available, with the motor, physiological or biomechanical functions of the human operator.
Further studies may make improvements to the illustrated technique, specifying
the optimal positioning of the sensors, defining the best AI system, but also proposing
the elaboration and development of other methods of ergonomic analysis, different from
those already used and accepted by classical ergonomics. An interesting aspect of the
study related to the topic presented here, and mainly focused on WMSDs, concerns the
interpretation of worker well-being as an integrated construct that includes physical,

Diagnostics 2022,12, 3048 17 of 21
psychosocial, and organizational aspects (1948 WHO definition of health). As it has, in fact,
been demonstrated by various studies, these aspects act with reciprocal influence on the
conditions of the human operator, and the intervention on one of the risk factors could
have repercussions on the other dimensions. This broadening of perspective also affects the
long-term benefits that can be prepared for, and guaranteed by, short-term investments in
improving occupational safety and health. Furthermore, given the multifactorial nature of
the underlying causes of WMSDs, a future study perspective could concern the assessment
of exposure associated with prolonged low-intensity static work, typical of teleworkers
and the increasing digitalization of work.
This article presented a systematic review of the combined use of wearable devices and
AI for ergonomic purposes, selecting 25 relevant studies from the scientific literature. The
analysis highlighted a deep interest, which has grown in recent years, for the use of wearable
sensors coupled with AI algorithms (both ML and DL) to monitor the biomechanical risk
to which workers are exposed to during their activities. The review provides the researcher
with an overview of the latest uses of AI and wearable sensors in the context of physical
ergonomics. Additionally, this review could be useful to support professionals in selecting
the most suitable wearable technology and AI strategy for ergonomic assessments and
improvements in industrial and non-industrial settings.
Author Contributions:
Conceptualization, L.D., N.P. and E.C.; methodology, L.D., G.C. and N.P.;
software, L.D.; validation, L.D., N.P. and E.C.; formal analysis, L.D. and N.P.; investigation, L.D. and
E.C.; resources, E.C.; data curation, N.P. and G.C.; writing—original draft preparation, L.D., G.C. and
N.P.; writing—review and editing, C.R., A.M.P. and E.C.; visualization, N.P. and G.C.; supervision,
L.D. and E.C.; project administration, E.C. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments:
Authors thank dott.ssa Monica Panigazzi for her advice and Ing. Claudia
Biondillo for her strong support in the selection of articles.
Conflicts of Interest: The authors declare no conflict of interest.
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