Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Ranavolo, Alberto; Draicchio, Francesco; Varrecchia, Tiwana; Silvetti, Alessio; Iavicoli, Sergio

doi:10.3390/ijerph15092001

Cited by 101 publications

(51 citation statements)

References 204 publications

(346 reference statements)

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“…In this discussion, we can also highlight that none of the studies performed a biomechanical risk assessment associated with the work task, although this would have been possible using both qualitative and quantitative approaches. For instance, lifting tasks could be classified using kinematic, kinetic, sEMG-based biomechanical risk assessment tools [ 9 , 13 , 70 , 71 , 72 , 73 ] and also by considering machine learning algorithms such as artificial neural networks [ 74 , 75 ]. The quantification of the risk associated with lifting tasks would have allowed us to relate the exoskeletons’ effectiveness to the risk levels.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Upper-Body Exoskeletons on Human Metabolic Cost and Thermal Response during Work Tasks—A Systematic Review

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Background: New wearable assistive devices (exoskeletons) have been developed for assisting people during work activity or rehabilitation. Although exoskeletons have been introduced into different occupational fields in an attempt to reduce the risk of work-related musculoskeletal disorders, the effectiveness of their use in workplaces still needs to be investigated. This systematic review focused on the effects of upper-body exoskeletons (UBEs) on human metabolic cost and thermophysiological response during upper-body work tasks. Methods: articles published until 22 September 2020 were selected from Scopus, Web of Science, and PubMed for eligibility and the potential risk of bias was assessed. Results: Nine articles resulted in being eligible for the metabolic aspects, and none for the thermal analysis. All the studies were based on comparisons between conditions with and without exoskeletons and considered a total of 94 participants (mainly males) performing tasks involving the trunk or overhead work, 7 back-support exoskeletons, and 1 upper-limb support exoskeleton. Eight studies found a significant reduction in the mean values of the metabolic or cardiorespiratory parameters considered and one found no differences. Conclusions: The reduction found represents a preliminary finding that needs to be confirmed in a wider range of conditions, especially in workplaces, where work tasks show different characteristics and durations compared to those simulated in the laboratory. Future developments should investigate the dependence of metabolic cost on specific UBE design approaches during tasks involving the trunk and the possible statistical correlation between the metabolic cost and the surface ElectroMyoGraphy (sEMG) parameters. Finally, it could be interesting to investigate the effect of exoskeletons on the human thermophysiological response.

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Section: Discussionmentioning

confidence: 99%

The Effects of Upper-Body Exoskeletons on Human Metabolic Cost and Thermal Response during Work Tasks—A Systematic Review

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“…Despite the many advantages of sEMG and HDsEMG, these instrumental tools are largely underexploited and their application lags expectations in the fields of ergonomics and occupational medicine. These tools have begun to be applied in the prevention of work-related musculoskeletal disorders, a set of painful inflammatory and degenerative conditions affecting the joints, spinal dizcs, cartilage, muscles, tendons, ligaments and peripheral nerves, caused by manual lifting, pushing and pulling, repetitive movements, and patients handling activities (32)(33)(34)(35)(36)(37)(38)(39). However, they remain underused in return-to-work rehabilitation plans for people with neurological pathologies with motor disorders.…”

Section: Bipolarmentioning

confidence: 99%

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human–Robot Collaborative Technologies

2020

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Patients affected by neurological pathologies with motor disorders when they are of working age have to cope with problems related to employability, difficulties in working, and premature work interruption. It has been demonstrated that suitable job accommodation plans play a beneficial role in the overall quality of life of pathological subjects. A well-designed return-to-work program should consider several recent innovations in the clinical and ergonomic fields. One of the instrument-based methods used to monitor the effectiveness of ergonomic interventions is surface electromyography (sEMG), a multi-channel, non-invasive, wireless, wearable tool, which allows in-depth analysis of motor coordination mechanisms. Although the scientific literature in this field is extensive, its use remains significantly underexploited and the state-of-the-art technology lags expectations. This is mainly attributable to technical and methodological (electrode-skin impedance, noise, electrode location, size, configuration and distance, presence of crosstalk signals, comfort issues, selection of appropriate sensor setup, sEMG amplitude normalization, definition of correct sEMG-related outcomes and normative data) and cultural limitations. The technical and methodological problems are being resolved or minimized also thanks to the possibility of using reference books and tutorials. Cultural limitations are identified in the traditional use of qualitative approaches at the expense of quantitative measurement-based monitoring methods to design and assess ergonomic interventions and train operators. To bridge the gap between the return-to-work rehabilitation and other disciplines, several teaching courses, accompanied by further electrodes and instrumentations development, should be designed at all Bachelor, Master and PhD of Science levels to enhance the best skills available among physiotherapists, occupational health and safety technicians and ergonomists.

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“…Among the proposed quantitative methods, the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) [3,[5][6][7][8][9] is an established means to assess risk of low back pain (LBP) due to manual lifting. More recently, in order to overcome equation and parameter restrictions [10][11][12][13][14][15][16][17], to increase the accuracy and minimize job misidentification [10,18] and to improve the identification of the relationship between WLBDs and risk factors [19], wearable monitoring devices have been proposed for biomechanical risk assessment [20,21]. Among these, inertial measurement units (IMUs), instrumented gloves, surface electromyography (sEMG) sensors, smart footwear-based wearable systems [22][23][24][25] and vision-based tracking systems [26] can monitor workers' motor behavior if individually placed on the body segments or embedded in elastic suits.…”

Section: Introductionmentioning

confidence: 99%

Lifting Activity Assessment Using Kinematic Features and Neural Networks

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Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features.

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Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review

Cited by 101 publications

References 204 publications

The Effects of Upper-Body Exoskeletons on Human Metabolic Cost and Thermal Response during Work Tasks—A Systematic Review

The Effects of Upper-Body Exoskeletons on Human Metabolic Cost and Thermal Response during Work Tasks—A Systematic Review

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human–Robot Collaborative Technologies

Lifting Activity Assessment Using Kinematic Features and Neural Networks

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