Next Article in Journal
Effect of Dexamethasone on the Incidence and Outcome of COVID-19 Associated Pulmonary Aspergillosis (CAPA) in Critically Ill Patients during First- and Second Pandemic Wave—A Single Center Experience
Previous Article in Journal
The Role of Active or Passive Drainage after Evacuation of Chronic Subdural Hematoma: An Analysis of Two Randomized Controlled Trials (cSDH-Drain-Trial and TOSCAN Trial)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 12
Issue 12
10.3390/diagnostics12123048
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

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

by
Leandro Donisi
1,2,*,†,
Giuseppe Cesarelli
1,2,†,
Noemi Pisani
3,*,
Alfonso Maria Ponsiglione
4,
Carlo Ricciardi
2,4 and
Edda Capodaglio
2
1
Department of Chemical, Materials and Production Engineering, University of Naples Federico II, 80125 Naples, Italy
2
Istituti Clinici Scientifici ICS Maugeri, 27100 Pavia, Italy
3
Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
4
Department of Information Technology and Electrical Engineering, University of Naples Federico II, 80125 Naples, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to the work.
Diagnostics 2022, 12(12), 3048; https://doi.org/10.3390/diagnostics12123048
Submission received: 14 October 2022 / Revised: 24 November 2022 / Accepted: 1 December 2022 / Published: 5 December 2022
(This article belongs to the Section Point-of-Care Diagnostics and Devices)
Browse Figures
Versions Notes

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.

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, physiological 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]. 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 possibility 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 execution 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 workplace [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 [12,23,24,25], 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 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].

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.

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.
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.

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) [59,60,61,62,63,64,65]. 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.
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].
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.
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.
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].
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.
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.
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), 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 overextension, 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, reinforcement 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 acceleration 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 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 investigated 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. Considering 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, 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.

References

  1. Punnett, L.; Cherniack, M.; Henning, R.; Morse, T.; Faghri, P. A Conceptual Framework for Integrating Workplace Health Promotion and Occupational Ergonomics Programs. Public Health Rep. 2009, 124, 16–25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Dul, J.; Bruder, R.; Buckle, P.; Carayon, P.; Falzon, P.; Marras, W.S.; Wilson, J.R.; van der Doelen, B. A Strategy for Human Factors/Ergonomics: Developing the Discipline and Profession. Ergonomics 2012, 55, 377–395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. What Is Ergonomics? Available online: https://www.ergonomics-fees.eu/node/7 (accessed on 10 October 2022).
  4. De Jonge, J.; Bosma, H.; Peter, R.; Siegrist, J. Job Strain, Effort-Reward Imbalance and Employee Well-Being: A Large-Scale Cross-Sectional Study. Soc. Sci. Med. 2000, 50, 1317–1327. [Google Scholar] [CrossRef] [PubMed]
  5. Hansson, T.; Jensen, I. Chapter 6: Sickness Absence Due to Back and Neck Disorders. Scand. J. Public Health 2004, 32, 109–151. [Google Scholar] [CrossRef]
  6. Da Costa, B.R.; Vieira, E.R. Risk Factors for Work-Related Musculoskeletal Disorders: A Systematic Review of Recent Longitudinal Studies. Am. J. Ind. Med. 2010, 53, 285–323. [Google Scholar] [CrossRef]
  7. Pedersen, J.; Schultz, B.B.; Madsen, I.E.H.; Solovieva, S.; Andersen, L.L. High Physical Work Demands and Working Life Expectancy in Denmark. Occup. Environ. Med. 2020, 77, 576–582. [Google Scholar] [CrossRef]
  8. Stobbe, T.J. Occupational Ergonomics and Injury Prevention. Occup. Med. 1996, 11, 531–543. [Google Scholar]
  9. Stanton, N.A.; Hedge, A.; Brookhuis, K.; Salas, E.; Hendrick, H.W. Handbook of Human Factors and Ergonomics Methods, 1st ed.; CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
  10. Inventory of Tools for Ergonomic Evaluation. Available online: https://www.torontomu.ca/content/dam/hfe/documents/ryerson_tool_inventory.pdf (accessed on 11 October 2022).
  11. Ometov, A.; Shubina, V.; Klus, L.; Skibińska, J.; Saafi, S.; Pascacio, P.; Flueratoru, L.; Gaibor, D.Q.; Chukhno, N.; Chukhno, O.; et al. A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges. Comput. Netw. 2021, 193, 108074. [Google Scholar] [CrossRef]
  12. Stefana, E.; Marciano, F.; Rossi, D.; Cocca, P.; Tomasoni, G. Wearable Devices for Ergonomics: A Systematic Literature Review. Sensors 2021, 21, 777. [Google Scholar] [CrossRef]
  13. Khakurel, J.; Melkas, H.; Porras, J. Tapping into the Wearable Device Revolution in the Work Environment: A Systematic Review. Inf. Technol. People 2018, 31, 791–818. [Google Scholar] [CrossRef] [Green Version]
  14. Svertoka, E.; Saafi, S.; Rusu-Casandra, A.; Burget, R.; Marghescu, I.; Hosek, J.; Ometov, A. Wearables for Industrial Work Safety: A Survey. Sensors 2021, 21, 3844. [Google Scholar] [CrossRef] [PubMed]
  15. Chidambaram, S.; Maheswaran, Y.; Patel, K.; Sounderajah, V.; Hashimoto, D.A.; Seastedt, K.P.; McGregor, A.H.; Markar, S.R.; Darzi, A. Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation. Sensors 2022, 22, 6920. [Google Scholar] [CrossRef] [PubMed]
  16. Nahavandi, D.; Alizadehsani, R.; Khosravi, A.; Acharya, U.R. Application of Artificial Intelligence in Wearable Devices: Opportunities and Challenges. Comput. Methods Programs Biomed. 2022, 213, 106541. [Google Scholar] [CrossRef]
  17. Chan, V.C.H.; Ross, G.B.; Clouthier, A.L.; Fischer, S.L.; Graham, R.B. The Role of Machine Learning in the Primary Prevention of Work-Related Musculoskeletal Disorders: A Scoping Review. Appl. Ergon. 2022, 98, 103574. [Google Scholar] [CrossRef] [PubMed]
  18. Van Eetvelde, H.; Mendonça, L.D.; Ley, C.; Seil, R.; Tischer, T. Machine Learning Methods in Sport Injury Prediction and Prevention: A Systematic Review. J. Exp. Ortop. 2021, 8, 1–15. [Google Scholar] [CrossRef]
  19. Andersen, L.L.; Vinstrup, J.; Sundstrup, E.; Skovlund, S.V.; Villadsen, E.; Thorsen, S.V. Combined Ergonomic Exposures and Development of Musculoskeletal Pain in the General Working Population: A Prospective Cohort Study. Scand. J. Work Environ. Health 2021, 47, 287–295. [Google Scholar] [CrossRef]
  20. Report—Expert Forecast on Emerging Physical Risks Related to Occupational Safety and Health. Available online: https://osha.europa.eu/sites/default/files/Report_-_Expert_forecast_on_emerging_physical_risks_related_to_occupational_safety_and_health.pdf (accessed on 11 October 2022).
  21. Pishgar, M.; Issa, S.F.; Sietsema, M.; Pratap, P.; Darabi, H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. Int. J. Environ. Res. Public Health 2021, 18, 6705. [Google Scholar] [CrossRef]
  22. Karwowski, W. Ergonomics and Human Factors: The Paradigms for Science, Engineering, Design, Technology and Management of Human-Compatible Systems. Ergonomics 2005, 48, 436–463. [Google Scholar] [CrossRef]
  23. López-Nava, I.H.; Muñoz-Meléndez, A. Wearable Inertial Sensors for Human Motion Analysis: A Review. IEEE Sens. J. 2016, 16, 7821–7834. [Google Scholar] [CrossRef]
  24. Ranavolo, A.; Draicchio, F.; Varrecchia, T.; Silvetti, A.; Iavicoli, S. Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review. Int. J. Environ. Res. Public Health 2018, 15, 2001. [Google Scholar] [CrossRef] [Green Version]
  25. Hafez, K. Input Variables for Manual Material Handling Assessment Methods Obtained Using Body Worn Sensors. J. Ergon. 2022, 12, 12. [Google Scholar]
  26. Lee, S.; Liu, L.; Radwin, R.; Li, J. Machine Learning in Manufacturing Ergonomics: Recent Advances, Challenges, and Opportunities. IEEE Robot. Autom. Lett. 2021, 6, 5745–5752. [Google Scholar] [CrossRef]
  27. Higgins, J.P.T.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. Cochrane Handbook for Systematic Reviews of Interventions, 2nd ed.; John Wiley & Sons: Hoboken, FL, USA, 2019. [Google Scholar]
  28. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. J. Clin. Epidemiol. 2009, 62, e1–e34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Kapicioglu, K.; Getmez, E.; Akbulut, B.E.; Akgul, A.; Ucar, B.; Kanlikilic, B.; Koc, M.; Gur, B. A Touchless Control Interface for Low-Cost ROVs. In Proceedings of the OCEANS 2021, San Diego, CA, USA, 21–23 September 2021; IEEE: Piscataway, NJ, USA, 2021. [Google Scholar]
  30. Izumi, K.; Minato, K.; Shiga, K.; Sugio, T.; Hanashiro, S.; Cortright, K.; Kudo, S.; Fujita, T.; Sado, M.; Maeno, T.; et al. Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design. Front. Psychiatry 2021, 12, 611243. [Google Scholar] [CrossRef]
  31. Bassani, G.; Filippeschi, A.; Avizzano, C.A. A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses. IEEE Sens. J. 2021, 21, 24731–24739. [Google Scholar] [CrossRef]
  32. Iosa, M.; Capodaglio, E.; Pelà, S.; Persechino, B.; Morone, G.; Antonucci, G.; Paolucci, S.; Panigazzi, M. Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients with Stroke Unable to Return to Work. Front. Neurol. 2021, 12, 561. [Google Scholar] [CrossRef]
  33. Mudiyanselage, S.E.; Nguyen, P.H.D.; Rajabi, M.S.; Akhavian, R. Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using SEMG Wearable Sensors and Machine Learning. Electronics 2021, 10, 2558. [Google Scholar] [CrossRef]
  34. Donisi, L.; Cesarelli, G.; Coccia, A.; Panigazzi, M.; Capodaglio, E.M.; D’Addio, G. Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning. Sensors 2021, 21, 2593. [Google Scholar] [CrossRef]
  35. Aiello, G.; Certa, A.; Abusohyon, I.; Longo, F.; Padovano, A. Machine Learning Approach towards Real Time Assessment of Hand-Arm Vibration Risk. In Proceedings of the 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021, Budapest, Hungary, 7–9 June 2021; International Federation of Automatic Control (IFAC): Laxenburg, Austria, 2021; pp. 1187–1192. [Google Scholar]
  36. Zhao, J.; Obonyo, E. Applying Incremental Deep Neural Networks-Based Posture Recognition Model for Ergonomics Risk Assessment in Construction. Adv. Eng. Inform. 2021, 50, 101374. [Google Scholar] [CrossRef]
  37. Matijevich, E.S.; Volgyesi, P.; Zelik, K.E. A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling. Sensors 2021, 21, 340. [Google Scholar] [CrossRef]
  38. Campero-Jurado, I.; Márquez-Sánchez, S.; Quintanar-Gómez, J.; Rodríguez, S.; Corchado, J.M. Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence. Sensors 2020, 20, 6241. [Google Scholar] [CrossRef] [PubMed]
  39. Akanmu, A.A.; Olayiwola, J.; Ogunseiju, O.; McFeeters, D. Cyber-Physical Postural Training System for Construction Workers. Autom. Constr. 2020, 117, 103272. [Google Scholar] [CrossRef]
  40. Umer, W.; Li, H.; Yantao, Y.; Antwi-Afari, M.F.; Anwer, S.; Luo, X. Physical Exertion Modeling for Construction Tasks Using Combined Cardiorespiratory and Thermoregulatory Measures. Autom. Constr. 2020, 112, 103079. [Google Scholar] [CrossRef]
  41. Conforti, I.; Mileti, I.; del Prete, Z.; Palermo, E. Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach. Sensors 2020, 20, 1557. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Zhao, J.; Obonyo, E. Convolutional Long Short-Term Memory Model for Recognizing Construction Workers’ Postures from Wearable Inertial Measurement Units. Adv. Eng. Inform. 2020, 46, 101177. [Google Scholar] [CrossRef]
  43. Antwi-Afari, M.; Li, H.; Umer, W.; Yu, Y.; Xing, X. Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System. J. Constr. Eng. Manag. 2020, 146, 04020077. [Google Scholar] [CrossRef]
  44. Asadi, H.; Zhou, G.; Lee, J.J.; Aggarwal, V.; Yu, D. A Computer Vision Approach for Classifying Isometric Grip Force Exertion Levels. Ergonomics 2020, 63, 1010–1026. [Google Scholar] [CrossRef]
  45. Estrada, J.E.; Vea, L.A. Real-Time Human Sitting Position Recognition Using Wireless Sensors. In Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing, Singapore, 20–22 March 2020; Association for Computing Machinery: New York, NY, USA, 2020; pp. 133–137. [Google Scholar]
  46. Fridolfsson, J.; Arvidsson, D.; Doerks, F.; Kreidler, T.J.; Grau, S. Workplace Activity Classification from Shoe-Based Movement Sensors. BMC Biomed. Eng. 2020, 2, 8. [Google Scholar] [CrossRef]
  47. Manjarres, J.; Narvaez, P.; Gasser, K.; Percybrooks, W.; Pardo, M. Physical Workload Tracking Using Human Activity Recognition with Wearable Devices. Sensors 2020, 20, 39. [Google Scholar] [CrossRef] [Green Version]
  48. Zhang, L.; Diraneyya, M.; Ryu, J.; Haas, C.; Abdel-Rahman, E. Automated Monitoring of Physical Fatigue Using Jerk. In Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC 2019), Banff, AB, Canada, 21–24 May 2019; International Association for Automation and Robotics in Construction: Oulu, Finland, 2019. [Google Scholar]
  49. Low, J.X.; Wei, Y.; Chow, J.; Ali, I.F.B. ActSen—AI-Enabled Real-Time IoT-Based Ergonomic Risk Assessment System. In Proceedings of the 2019 IEEE International Congress on Internet of Things (ICIOT), Milan, Italy, 8–13 July 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 76–78. [Google Scholar]
  50. Lim, S.; D’Souza, C. Gender and Parity in Statistical Prediction of Anterior Carry Hand-Loads from Inertial Sensor Data. In Proceedings of the Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Seattle, WA, USA, 28 October–1 November 2019; SAGE Publications Inc: Los Angeles, CA, USA, 2019; pp. 1142–1146. [Google Scholar]
  51. Xie, X.; Chang, Z. Intelligent Wearable Occupational Health Safety Assurance System of Power Operation. J. Med. Syst. 2018, 43, 1–7. [Google Scholar] [CrossRef]
  52. Martire, T.; Nazemzadeh, P.; Sanna, A.; Trojaniello, D. Digital Screen Detection Enabled by Wearable Sensors: Application in ADL Settings. In Proceedings of the 2018 International Conference on Intelligent Systems (IS), Madeira, Portugal, 25–27 September 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 584–588. [Google Scholar]
  53. Antwi-Afari, M.F.; Li, H.; Yu, Y.; Kong, L. Wearable Insole Pressure System for Automated Detection and Classification of Awkward Working Postures in Construction Workers. Autom. Constr. 2018, 96, 433–441. [Google Scholar] [CrossRef]
  54. Nath, N.D.; Chaspari, T.; Behzadan, A.H. Automated Ergonomic Risk Monitoring Using Body-Mounted Sensors and Machine Learning. Adv. Eng. Inform. 2018, 38, 514–526. [Google Scholar] [CrossRef]
  55. Yu, Y.; Li, H.; Yang, X.; Umer, W. Estimating Construction Workers’ Physical Workload by Fusing Computer Vision and Smart Insole Technologies. In Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, 20–25 July 2018; International Association on Automation and Robotics in Construction: Oulu, Finland, 2018; pp. 1212–1219. [Google Scholar]
  56. Raso, R.; Emrich, A.; Burghardt, T.; Schlenker, M.; Gudehus, T.; Sträter, O.; Fettke, P.; Loos, P. Activity Monitoring Using Wearable Sensors in Manual Production Processes—An Application of CPS for Automated Ergonomic Assessments. In Proceedings of the Multikonferenz Wirtschaftsinformatik 2018 (MKWI 2018), Lüneburg, Germany, 6–9 March 2018; Leuphana Universität Lüneburg: Lüneburg, Germany, 2018. [Google Scholar]
  57. Olsen, G.F.; Brilliant, S.S.; Primeaux, D.; Najarian, K. Signal Processing and Machine Learning for Real-Time Classification of Ergonomic Posture with Unobtrusive on-Body Sensors; Application in Dental Practice. In Proceedings of the 2009 ICME International Conference on Complex Medical Engineering, Tempre, AZ, USA, 9–11 April 2009; IEEE: Piscataway, NJ, USA, 2009. [Google Scholar]
  58. Motti, V.G.; Caine, K. Human Factors Considerations in the Design of Wearable Devices. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Chicago, IL, USA, 27–31 October 2014; Sage Publications: Los Angeles, CA, USA, 2014; pp. 1820–1824. [Google Scholar]
  59. Motti, V.G.; Caine, K. An Overview of Wearable Applications for Healthcare: Requirements and Challenges. In Proceedings of the 2015 Association for Computing Machinery International Joint Conference on Pervasive and Ubiquitous Computing and 2015 Association for Computing Machinery International Symposium on Wearable Computers (UbiComp/ISWC’15 Adjunct), Osaka, Japan, 7–11 September 2015; Association for Computing Machinery: New York, NY, USA, 2015; pp. 635–641. [Google Scholar]
  60. D’Addio, G.; Donisi, L.; Mercogliano, L.; Cesarelli, G.; Bifulco, P.; Cesarelli, M. Potential Biomechanical Overload on Skeletal Muscle Structures in Students During Walk with Backpack. In Proceedings of the XV Mediterranean Conference on Medical and Biological Engineering and Computing—MEDICON 2019; Henriques, J., Neves, N., de Carvalho, P., Eds.; Springer: Cham, Switzerland, 2019; pp. 262–266. [Google Scholar]
  61. Donisi, L.; Coccia, A.; Amitrano, F.; Mercogliano, L.; Cesarelli, G.; D’Addio, G. Backpack Influence on Kinematic Parameters Related to Timed Up and Go (TUG) Test in School Children. In Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy, 1 June–1 July 2020; IEEE: Piscataway, NJ, USA, 2020. [Google Scholar]
  62. Pagano, G.; D’Addio, G.; de Campi, M.; Donisi, L.; Biancardi, A.; Cesarelli, M. Rehabilitation Outcome in Patients Undergone Hip or Knee Replacement Surgery Using Inertial Technology for Gait Analysis. In Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy, 1 June–1 July 2020; IEEE: Piscataway, NJ, USA, 2020. [Google Scholar]
  63. D’Addio, G.; Dionisi, L.; Pagano, G.; Mercogliano, L.; Cesarelli, M.; Cesarelli, G. Effects of the school backpack on walking kinematics: A mechanical overload potentially causing musculoskeletal disorders in developmental age? G. Ital. Med. Lav. Ergon. 2020, 42, 201–207. [Google Scholar] [PubMed]
  64. Donisi, L.; Amitrano, F.; Coccia, A.; Mercogliano, L.; Cesarelli, G.; D’Addio, G. Influence of the Backpack on School Children’s Gait: A Statistical and Machine Learning Approach. In Proceedings of the 8th European Medical and Biological Engineering Conference, Portorož, Slovenia, 29 November–3 December 2020; Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D., Eds.; Springer: Cham, Switzerland, 2021; pp. 682–688. [Google Scholar]
  65. Donisi, L.; Cesarelli, G.; Balbi, P.; Provitera, V.; Lanzillo, B.; Coccia, A.; D’Addio, G. Positive Impact of Short-Term Gait Rehabilitation in Parkinson Patients: A Combined Approach Based on Statistics and Machine Learning. Math. Biosci. Eng. 2021, 18, 6995–7009. [Google Scholar] [CrossRef] [PubMed]
  66. Donisi, L.; Pagano, G.; Cesarelli, G.; Coccia, A.; Amitrano, F.; D’Addio, G. Benchmarking between Two Wearable Inertial Systems for Gait Analysis Based on a Different Sensor Placement Using Several Statistical Approaches. Measurement 2021, 173, 108642. [Google Scholar] [CrossRef]
  67. D’Addio, G.; Donisi, L.; Pagano, G.; Improta, G.; Biancardi, A.; Cesarelli, M. Agreement between Opal and G-Walk Wearable Inertial Systems in Gait Analysis on Normal and Pathological Subjects. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; IEEE: Piscataway, NJ, USA, 2019. [Google Scholar]
  68. Iosa, M.; Picerno, P.; Paolucci, S.; Morone, G. Wearable Inertial Sensors for Human Movement Analysis. Expert Rev. Med. Devices 2016, 13, 641–659. [Google Scholar] [CrossRef]
  69. O’Reilly, M.; Caulfield, B.; Ward, T.; Johnston, W.; Doherty, C. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review. Sports Med. 2018, 48, 1221–1246. [Google Scholar] [CrossRef] [Green Version]
  70. Rajendran, M.; Sajeev, A.; Shanmugavel, R.; Rajpradeesh, T. Ergonomic Evaluation of Workers during Manual Material Handling. In Proceedings of the 3rd International Conference on Materials, Manufacturing and Modelling, Vellore, India, 19–21 March 2021; Vellore Institute of Technology: Vellore, India, 2021; pp. 7770–7776. [Google Scholar]
  71. Lu, M.-L.; Waters, T.R.; Krieg, E.; Werren, D. Efficacy of the Revised NIOSH Lifting Equation to Predict Risk of Low-Back Pain Associated With Manual Lifting: A One-Year Prospective Study. Hum. Factors 2014, 56, 73–85. [Google Scholar] [CrossRef]
  72. Donisi, L.; Capodaglio, E.; Pagano, G.; Amitrano, F.; Cesarelli, M.; Panigazzi, M.; D’Addio, G. Feasibility of Tree-Based Machine Learning Algorithms Fed with Surface Electromyographic Features to Discriminate Risk Classes According to NIOSH. In Proceedings of the 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Taormina, Italy, 22–24 June 2022; IEEE: Piscataway, NJ, USA, 2022. [Google Scholar]
  73. Ramadhani, M.; Rukman, R.; Prayogo, D.; Ayu, D. Assessment Analysis of Ergonomics Work Posture on Wheel Installation with Ovako Work Posture Analysis System (OWAS) Method and Rapid Entire Body Assesment (REBA) Method Preventing Musculoskeleal Disorders AT Perum PPD Jakarta. IOSR J. Humanit Soc. Sci. 2018, 23, 1–11. [Google Scholar]
  74. Delp, L.; Mojtahedi, Z.; Sheikh, H.; Lemus, J. A Legacy of Struggle: The OSHA Ergonomics Standard and Beyond, Part I. New Solut. 2014, 24, 365–389. [Google Scholar] [CrossRef]
  75. Saini, R.; Ghosh, S.K. Ensemble Classifiers in Remote Sensing: A Review. In Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 5–6 May 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1148–1152. [Google Scholar]
  76. Mahesh, B. Machine Learning Algorithms—A Review. Int. J. Sci. Res. 2020, 9, 381–386. [Google Scholar] [CrossRef]
  77. Sydenham, P.H.; Thorn, R. Handbook of Measuring System Design, 1st ed.; Wiley: Chichester, UK, 2005. [Google Scholar]
  78. Weinberger, K.Q.; Saul, L.K. Distance Metric Learning for Large Margin Nearest Neighbor Classification. J. Mach. Learn. Res. 2009, 10, 207–244. [Google Scholar]
  79. Murthy, S.K. Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Min. Knowl. Discov. 1998, 2, 345–389. [Google Scholar] [CrossRef]
  80. Kumar, A.; Naughton, J.; Patel, J.M. Learning Generalized Linear Models Over Normalized Data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, VI, Australia, 31 May–4 June 2015; Association for Computing Machinery: New York, NY, USA, 2015; pp. 1969–1984. [Google Scholar]
  81. Al-Aidaroos, K.M.; Bakar, A.A.; Othman, Z. Naïve Bayes Variants in Classification Learning. In Proceedings of the 2010 International Conference on Information Retrieval & Knowledge Management (CAMP), Shah Alam, Malaysia, 17–18 March 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 276–281. [Google Scholar]
  82. Zhang, H.; Yan, X.; Li, H. Ergonomic Posture Recognition Using 3D View-Invariant Features from Single Ordinary Camera. Autom. Constr. 2018, 94, 1–10. [Google Scholar] [CrossRef]
  83. Tsao, L.; Li, L.; Ma, L. Human Work and Status Evaluation Based on Wearable Sensors in Human Factors and Ergonomics: A Review. IEEE Trans. Hum. Mach. Syst. 2019, 49, 72–84. [Google Scholar] [CrossRef]
Diagnostics 12 03048 g001 550
Figure 1. Summary review workflow.
Figure 1. Summary review workflow.
Diagnostics 12 03048 g001
Diagnostics 12 03048 g002 550
Figure 2. Distribution of papers over time.
Figure 2. Distribution of papers over time.
Diagnostics 12 03048 g002
Diagnostics 12 03048 g003 550
Figure 3. Safety assurance system framework. Reproduced with permission from [51] published by Springer Nature, 2019.
Figure 3. Safety assurance system framework. Reproduced with permission from [51] published by Springer Nature, 2019.
Diagnostics 12 03048 g003
Diagnostics 12 03048 g004 550
Figure 4. (a) Opal System; (b) Opal sensor positioning. Reproduced with permission from [34]; published by Multidisciplinary Digital Publishing Institute, 2021.
Figure 4. (a) Opal System; (b) Opal sensor positioning. Reproduced with permission from [34]; published by Multidisciplinary Digital Publishing Institute, 2021.
Diagnostics 12 03048 g004
Diagnostics 12 03048 g005 550
Figure 5. Distributions of wearable sensors on anatomical segments of human body.
Figure 5. Distributions of wearable sensors on anatomical segments of human body.
Diagnostics 12 03048 g005
Diagnostics 12 03048 g006 550
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 12 03048 g006
Diagnostics 12 03048 g007 550
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.
Diagnostics 12 03048 g007
Diagnostics 12 03048 g008 550
Figure 8. Employed algorithm distributions.
Figure 8. Employed algorithm distributions.
Diagnostics 12 03048 g008
Table
Table 1. Analysis of the studies included in the review.
Table 1. Analysis of the studies included in the review.
StudyScopePopulationSensor
(Positioning)		Signal
Acquired		TaskErgonomic
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 data1 volunteer healthy subject2 wireless sEMG muscle sensors
(Thoracic and Multifidus muscles)		EMG signalLifting loadsRNLEML and DL
(RF, DT, GB, AB, KNN, NB, SVM, LR, MP)		Weight, horizontal location of the object relative to the body, Min, Median, SDAll 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 algorithms7 volunteer healthy subjects1 IMU sensor
(Lumbar region)		Linear Acceleration, Angular velocityLifting loadsRNLEML and DL
(RF, DT, GB, AB, KNN, NB, SVM, LR, MP)		RMS, SD, Min, MaxRF 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 classifierworker healthy subjects (nns)2 accelerometers
(Wrists)		Linear AccelerationRotating 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 DL9 worker healthy subjects5 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 handling10 volunteer healthy subjectsIMU sensors, Pressure insoles
(Feet, Shanks, Thighs, Pelvis, Trunk)		Linear Acceleration, Angular velocity, Foot plantar pressureManual material handlingNSML and DL
(GLMs, SVMs, NNs, GBDT)		Kinematic and kinetic featuresGBDT 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 algorithms1 worker healthy subjectLight sensor, Shock sensor, Accelerometer and Gyroscope
(Head)		Atmospheric pressure, Environment temperature, Humidity, Brightness, Shock alerts, Linear Acceleration, Angular velocityGeneric 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 qualitySVM 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 postures10 volunteer healthy subjects19 IMU sensors, Virtual reality head-mounted display
(Head, Arms, Thorax, Waist, Legs)		Linear Acceleration, Angular velocity, ImageConstruction work activities PERA ML
(Reinforcement learning algorithm)		Kinematic featuresNumerical results not provided
(color feedback associated to the risk level)
Umer et al.
(2020)
[40]		Predicting physical exertion levels using multiple physiological measures10 volunteer healthy subjectsECG sensor, Skin temperature sensor, Respiration sensor
(Thorax)		ECG, Skin temperature, RespirationConstruction work activities (manual material handling)Borg-20 scaleML
(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 data26 volunteer healthy subjects8 IMU sensors
(Sternum, Pelvis, Thighs, Shanks, Feet)		Linear Acceleration, Angular velocityManual material handlingNSML
(SVM)		Kinematic featuresSVM showed an accuracy of 99.4%
Zhao & Obonyo
(2020)
[42]		Proposing a CLSTM model for recognizing construction workers’ postures4 worker healthy subjects5 IMU sensors
(Forehead, Chest center, Right upper arm, Right thigh, Right crus)		Linear Acceleration, Angular velocityConstruction work activitiesOWASDL
(CLSTM)		NSMacro 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 system2 volunteer healthy subjects13 capacitive sensors, Accelerometer
(Feet)		Foot plantar pressure, Linear AccelerationManual material handlingOSHAML 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 subjectsHand dynamometer, Pulse oximeter
(Hand)		Grip force, PPGIsometric force exertionsMoore-Garg Strain IndexML 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
Estrada & Vea
(2020)
[45]		Recognizing proper and improper sitting posture to the laptop60 volunteer healthy subjects10 flex sensors
(Upper body)		BendingSittingNSML
(DT)		Gender, Age, Height, Weight, Wrist size, Category, Chair height, Distance, Bending featuresDT 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 AccelerationSitting, Standing, Walking,
Weight carrying, Kneeling;
logistics warehouse and industrial production activities		NSML
(RF, SVM, KNN)		Mean, SD, Skewness, Kurtosis, Energy, CorrelationRF 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 data29 volunteer healthy subjectsAccelerometer, PPG sensor
(Hip, Wrist)		Linear Acceleration, PPGJogging, Doing crunches, Push-ups, Squatting, StandingFirmat’s scoreML
(RF, KNN)		Mean, SD, Variance, Median absolute deviationThe 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 classifiers6 worker healthy subjects17 IMU sensors
(Pelvis, Sternum, Head,
Both shoulders, Upper arms, Lower arms, Hands, Upper legs, Lower legs, Feet)		Linear Acceleration, Angular velocityBricklaying activitiesNSML
(SVM)		Mean, SD, Max, Min, Jerk cost, Dominant frequencyThe SVM classifier showed an accuracy over 80%
Low et al.
(2019)
[49]		Classifying workers movement using ML algorithm by acquiring accelerometer data5 volunteer healthy subjectsAccelerometer
(Waist, Wrist)		Linear AccelerationBending full forward, Bending midway forward, Squatting, TwistingRodger Muscle Fatigue AnalysisML
(LinR, LR)		Accelerometer featuresLR 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 data22 volunteer healthy subjects3 inertial sensors (Thorax, Lumbar, Shank)Linear Acceleration, Angular velocityCarrying a boxNSML
(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
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 environment1 worker healthy subjectGyroscope 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, PPGRoutine work tasks (electric substation)NSML
(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 activities5 healthy subjects (ns volunteer or worker)1 color light sensor
(Forehead)		BrightnessOffice activities (read documents or papers, simulate a lesson etc.) NSML
(RF, NB)		NSThe 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 data10 volunteer healthy subjects13 capacitive sensors, Accelerometer
(Feet)		Foot plantar pressure, Linear AccelerationConstruction work activities ISO 11226:2000ML 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 data2 worker healthy subjectsIMU sensors
(Upper arm, Waist)		Linear Acceleration, Angular velocityWarehouse operations (lift, lower, carry, push, pull)OSHAML
(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 insolesworker healthy subjects (nns)Pressure sensors, IMU sensors
(Feet, Total body)		Foot plantar pressure, Linear Acceleration, Angular velocityMaterial handling, Rebar, PlasteringNSDL
(NS)		NSNS numeric 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 EAWS15 worker healthy subjectsStrain sensors, Pressure sensors(Upper body
(Trunk and arms))		Deformation
Pressure		Lifting loads, Drive, SittingEAWSML
(ns)		NSNS numeric results
Olsen et al.
(2009)
[57]		Classifying correct and incorrect postures using ML techniques to improve the ergonomics of dental practitioners11 healthy subjects (ns volunteer or worker)3 inclinometers
(Shoulder blades, Lower back)		AnglesRoutine work tasks (leaning left, leaning right, leaning forwards and backwards, and slouching)NSML and DL
(AB, SVM, LVQ, KNN, ANN)		Inclinometers features from x and y axesThe 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.
Table
Table 2. Inertial sensors and complementary wearable sensors’ distributions.
Table 2. Inertial sensors and complementary wearable sensors’ distributions.
StudyInertial SensorComplementary 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.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

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

AMA Style

Donisi L, Cesarelli G, Pisani N, Ponsiglione AM, Ricciardi C, Capodaglio E. Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics. 2022; 12(12):3048. https://doi.org/10.3390/diagnostics12123048

Chicago/Turabian Style

Donisi, Leandro, Giuseppe Cesarelli, Noemi Pisani, Alfonso Maria Ponsiglione, Carlo Ricciardi, and Edda Capodaglio. 2022. "Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature" Diagnostics 12, no. 12: 3048. https://doi.org/10.3390/diagnostics12123048

APA Style

Donisi, L., Cesarelli, G., Pisani, N., Ponsiglione, A. M., Ricciardi, C., & Capodaglio, E. (2022). Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics, 12(12), 3048. https://doi.org/10.3390/diagnostics12123048

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop