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Article type: Research Article
Authors: Kumar Banga, Harisha; * | Kumar, Rajb | Kalra, Parveenb
Affiliations: [a] Fashion and Lifestyle Accessory Design Department, National Institute of Fashion Technology, Mumbai, India | [b] Department of Production and Industrial Engineering, Punjab Engineering College, Chandigarh, India
Correspondence: [*] Address for correspondence: Harish Kumar Banga, Fashion and Lifestyle Accessory Design Department, National Institute of Fashion Technology, Mumbai, India. E-mail: drhkbanga@gmail.com.
Abstract: BACKGROUND:Nowadays, real-time motion tracking devices are widely used for ergonomic assessment of several manual quotidian activities. The real-time tracking of human activities makes it easier to observe the exposure of work-related musculoskeletal disorders (WMSDs) in the human body. OBJECTIVE:This study aims to determine the suitability of a real-time motion tracking device (Kinect v1 interfaced with a commercial ergonomic assessment software, JACK) for real-time ergonomic evaluation of the strenuous operation of the manual lawn mower. METHOD:The lawn mower operators perform various strenuous activities while operating the manual lawn mower for long intervals of time, which causes WMSDs in the entire body of the operators. These working operators’ activities have been captured using Kinect v1 interfaced with JACK, to address the ergonomic issues responsible for the whole-body WMSDs. The forces acting on the lower back, Rapid Upper Limb Assessment score and static strength have been predicted using JACK. RESULTS:This study proves the exposure of the operators towards the whole-body WMSDs while operating the manual lawn mower. CONCLUSION:The findings provide a quick and straightforward approach for performing the real-time ergonomic evaluation of any operation, which can help the industrial staff estimate the risk of level WMSDs.
Keywords: Work-related musculoskeletal disorders, rapid upper limb assessment, lower back analysis, static strength prediction
DOI: 10.3233/WOR-210713
Journal: Work, vol. 72, no. 2, pp. 497-510, 2022
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