Physical risk factors identification based on body sensor network combined to videotaping.
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...
| Publicado en: | Applied Ergonomics Vol. 65; pp. 410 - 418 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Nov2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124577790&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124577790 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00036870 ALO jtl: Applied Ergonomics issn: 00036870 maglogo: N pubinfo: dt: Nov2017 vid: 65 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 124577790 124577790 NLM28528627 10.1016/j.apergo.2017.05.003 NLM28528627 124577790 ppf: 410 ppct: 8 formats: tig: atl: Physical risk factors identification based on body sensor network combined to videotaping. aug: au: Vignais, Nicolas Bernard, Fabien Touvenot, Gérard Sagot, Jean-Claude affil: ERCOS Research Unit, Systems & Transport Laboratory, University of Technology of Belfort-Montbéliard, 91010 Belfort, France sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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