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author:

Li, Jianwei (Li, Jianwei.) [1] (Scholars:李建微) | Huang, Sihan (Huang, Sihan.) [2] | Wang, Faming (Wang, Faming.) [3] | Chen, Sixi (Chen, Sixi.) [4] (Scholars:陈思喜) | Zheng, Huiru (Zheng, Huiru.) [5]

Indexed by:

EI SCIE

Abstract:

Musculoskeletal disorders (MSDs) are associated with sitting postures. The assessment and prevention of risk factors for workplace exposure are indispensable aspects of reducing the occurrence of MSDs. This paper proposes an ergonomic assessment method of risk factors for MSDs associated with sitting postures in the actual working conditions. A Kinect sensor with the RULA method was primarily used to collect the data and evaluate the relevant postures. The results obtained were compared with the evaluation results by a human expert. Additionally, we verified the capability and effectiveness of this method. A program system for human joint recognition and acquisition was implemented. The results indicated that the Kinect joint data is generally accurate and can adequately complete the RULA evaluation table. The results from the front and right-hand side obtained by the Kinect were consistent with the results of the expert evaluation, and no significant difference was observed between them (p > 0.05). However, when the participants faced the Kinect, the sensor performed better, and the evaluation result was more accurate. A high consistency was observed between the evaluation results obtained from the front and the expert (proportion agreement index = 0.65, Cohen's kappa = 0.77). Only a slight consistency was observed between the evaluation results obtained from the right-hand side and the expert (proportion agreement index = 0.41, Cohen's kappa = 0.08). This research created a new ergonomic method for the risk assessment of MSDs associated with sitting postures. The combination of theory and practice is crucial in the risk assessment of sitting postures in workplaces.

Keyword:

ergonomics Kinect RULA Sitting posture assessment

Community:

  • [ 1 ] [Li, Jianwei]Fuzhou Univ, Coll Phys & Informat Engn, 2 Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
  • [ 2 ] [Huang, Sihan]Fuzhou Univ, Coll Phys & Informat Engn, 2 Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
  • [ 3 ] [Wang, Faming]Fuzhou Univ, Coll Phys & Informat Engn, 2 Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
  • [ 4 ] [Chen, Sixi]Fuzhou Univ, Coll Phys & Informat Engn, 2 Xueyuan Rd, Fuzhou 350116, Fujian, Peoples R China
  • [ 5 ] [Zheng, Huiru]Ulster Univ, Sch Comp, Shore Rd, Newtownabbey BT370QB, Antrim, North Ireland

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Source :

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

ISSN: 0218-0014

Year: 2022

Issue: 09

Volume: 36

1 . 5

JCR@2022

0 . 9 0 0

JCR@2023

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:61

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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