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Physical risk factors identification based on body sensor network combined to videotaping.
Appl Ergon. 2017 Nov; 65:410-417.AE

Abstract

The aim of this study was to perform an ergonomic analysis of a material handling task by combining a subtask video analysis and a RULA computation, implemented continuously through a motion capture system combining inertial sensors and electrogoniometers. Five workers participated to the experiment. Seven inertial measurement units, placed on the worker's upper body (pelvis, thorax, head, arms, forearms), were implemented through a biomechanical model of the upper body to continuously provide trunk, neck, shoulder and elbow joint angles. Wrist joint angles were derived from electrogoniometers synchronized with the inertial measurement system. Worker's activity was simultaneously recorded using video. During post-processing, joint angles were used as inputs to a computationally implemented ergonomic evaluation based on the RULA method. Consequently a RULA score was calculated at each time step to characterize the risk of exposure of the upper body (right and left sides). Local risk scores were also computed to identify the anatomical origin of the exposure. Moreover, the video-recorded work activity was time-studied in order to classify and quantify all subtasks involved into the task. Results showed that mean RULA scores were at high risk for all participants (6 and 6.2 for right and left sides respectively). A temporal analysis demonstrated that workers spent most part of the work time at a RULA score of 7 (right: 49.19 ± 35.27%; left: 55.5 ± 29.69%). Mean local scores revealed that most exposed joints during the task were elbows, lower arms, wrists and hands. Elbows and lower arms were indeed at a high level of risk during the total time of a work cycle (100% for right and left sides). Wrist and hands were also exposed to a risky level for much of the period of work (right: 82.13 ± 7.46%; left: 77.85 ± 12.46%). Concerning the subtask analysis, subtasks called 'snow thrower', 'opening the vacuum sealer', 'cleaning' and 'storing' have been identified as the most awkward for right and left sides given mean RULA scores and percentages of time spent at risky levels. Results analysis permitted to suggest ergonomic recommendations for the redesign of the workstation. Contributions of the proposed innovative system dedicated to physical ergonomic assessment are further discussed.

Authors+Show Affiliations

ERCOS Research Unit, Systems & Transport Laboratory, University of Technology of Belfort-Montbéliard, 91010 Belfort, France; CIAMS, Univ. Paris-Sud, Université Paris-Saclay, 91405 Orsay Cedex, France; CIAMS, Université d'Orléans, 45067 Orléans, France. Electronic address: nicolas.vignais@u-psud.fr.ERCOS Research Unit, Systems & Transport Laboratory, University of Technology of Belfort-Montbéliard, 91010 Belfort, France. Electronic address: fabien.bernard@utbm.fr.ERCOS Research Unit, Systems & Transport Laboratory, University of Technology of Belfort-Montbéliard, 91010 Belfort, France. Electronic address: gerard.touvenot@utbm.fr.ERCOS Research Unit, Systems & Transport Laboratory, University of Technology of Belfort-Montbéliard, 91010 Belfort, France. Electronic address: jean-claude.sagot@utbm.fr.

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

28528627

Citation

Vignais, Nicolas, et al. "Physical Risk Factors Identification Based On Body Sensor Network Combined to Videotaping." Applied Ergonomics, vol. 65, 2017, pp. 410-417.
Vignais N, Bernard F, Touvenot G, et al. Physical risk factors identification based on body sensor network combined to videotaping. Appl Ergon. 2017;65:410-417.
Vignais, N., Bernard, F., Touvenot, G., & Sagot, J. C. (2017). Physical risk factors identification based on body sensor network combined to videotaping. Applied Ergonomics, 65, 410-417. https://doi.org/10.1016/j.apergo.2017.05.003
Vignais N, et al. Physical Risk Factors Identification Based On Body Sensor Network Combined to Videotaping. Appl Ergon. 2017;65:410-417. PubMed PMID: 28528627.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Physical risk factors identification based on body sensor network combined to videotaping. AU - Vignais,Nicolas, AU - Bernard,Fabien, AU - Touvenot,Gérard, AU - Sagot,Jean-Claude, Y1 - 2017/05/18/ PY - 2016/08/15/received PY - 2017/05/04/revised PY - 2017/05/04/accepted PY - 2017/5/23/pubmed PY - 2018/5/10/medline PY - 2017/5/23/entrez KW - Inertial measurement unit KW - Manual tasks KW - Musculoskeletal disorders KW - Physical ergonomics KW - Risk of exposure SP - 410 EP - 417 JF - Applied ergonomics JO - Appl Ergon VL - 65 N2 - The aim of this study was to perform an ergonomic analysis of a material handling task by combining a subtask video analysis and a RULA computation, implemented continuously through a motion capture system combining inertial sensors and electrogoniometers. Five workers participated to the experiment. Seven inertial measurement units, placed on the worker's upper body (pelvis, thorax, head, arms, forearms), were implemented through a biomechanical model of the upper body to continuously provide trunk, neck, shoulder and elbow joint angles. Wrist joint angles were derived from electrogoniometers synchronized with the inertial measurement system. Worker's activity was simultaneously recorded using video. During post-processing, joint angles were used as inputs to a computationally implemented ergonomic evaluation based on the RULA method. Consequently a RULA score was calculated at each time step to characterize the risk of exposure of the upper body (right and left sides). Local risk scores were also computed to identify the anatomical origin of the exposure. Moreover, the video-recorded work activity was time-studied in order to classify and quantify all subtasks involved into the task. Results showed that mean RULA scores were at high risk for all participants (6 and 6.2 for right and left sides respectively). A temporal analysis demonstrated that workers spent most part of the work time at a RULA score of 7 (right: 49.19 ± 35.27%; left: 55.5 ± 29.69%). Mean local scores revealed that most exposed joints during the task were elbows, lower arms, wrists and hands. Elbows and lower arms were indeed at a high level of risk during the total time of a work cycle (100% for right and left sides). Wrist and hands were also exposed to a risky level for much of the period of work (right: 82.13 ± 7.46%; left: 77.85 ± 12.46%). Concerning the subtask analysis, subtasks called 'snow thrower', 'opening the vacuum sealer', 'cleaning' and 'storing' have been identified as the most awkward for right and left sides given mean RULA scores and percentages of time spent at risky levels. Results analysis permitted to suggest ergonomic recommendations for the redesign of the workstation. Contributions of the proposed innovative system dedicated to physical ergonomic assessment are further discussed. SN - 1872-9126 UR - https://www.unboundmedicine.com/medline/citation/28528627/Physical_risk_factors_identification_based_on_body_sensor_network_combined_to_videotaping_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0003-6870(17)30106-0 DB - PRIME DP - Unbound Medicine ER -