A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model

Young-Jin Kwon; Do-Hyun Kim; Byung-Chang Son; Kyoung-Ho Choi; Sungbok Kwak; Taehong Kim

doi:10.3390/ijerph19169803

A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model

Int J Environ Res Public Health. 2022 Aug 9;19(16):9803. doi: 10.3390/ijerph19169803.

Authors

Young-Jin Kwon^{1

2}, Do-Hyun Kim¹, Byung-Chang Son³, Kyoung-Ho Choi⁴, Sungbok Kwak⁵, Taehong Kim²

Affiliations

¹ Intelligent Robotics Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea.
² School of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Korea.
³ Department of Rehabilitation Technology, Korea Nazarene University, Cheonan 31172, Korea.
⁴ Department of Electronics Engineering, Mokpo National University, Muan 58554, Korea.
⁵ Advanced Engineering Team, Duckyang Co., Ltd., Suwon 16229, Korea.

Abstract

Musculoskeletal disorders are an unavoidable occupational health problem. In particular, workers who perform repetitive tasks onsite in the manufacturing industry suffer from musculoskeletal problems. In this paper, we propose a system that evaluates the posture of workers in the manufacturing industry with single-view 3D human pose-estimation that can estimate the posture in 3D using an RGB camera that can easily acquire the posture of a worker in a complex workplace. The proposed system builds a Duckyang-Auto Worker Health Safety Environment (DyWHSE), a manufacturing-industry-specific dataset, to estimate the wrist pose evaluated by the Rapid Limb Upper Assessment (RULA). Additionally, we evaluate the quality of the built DyWHSE dataset using the Human3.6M dataset, and the applicability of the proposed system is verified by comparing it with the evaluation results of the experts. The proposed system provides quantitative assessment guidance for working posture risk assessment, assisting the continuous posture assessment of workers.

Keywords: RULA; WMSDs; dataset; deep learning; pose estimation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Manufacturing Industry
Musculoskeletal Diseases* / epidemiology
Occupational Diseases* / diagnosis
Occupational Diseases* / epidemiology
Posture
Risk Assessment / methods

Grants and funding

This work was funded by the Technology Innovation Program (grant number: 20007047, the Development of HSE Technology for Working Process-Linked Workers in the Industrial Field) and (grant number: 20016250, the Development of Intention-Recognition Intelligent Technology Based on User Multi-Data for Non-Face-to-Face Devices) funded by the Ministry of Trade, Industry, and Energy (MOTIE, KOREA).