지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.4
- 수록면
- 75 - 87 (13page)
- DOI
- 10.5143/JESK.2021.40.2.75
이용수
초록· 키워드
오류제보하기
Objective: This study was intended to develop two risk assessment systems that can evaluate OWAS scores and action level with good accuracy and reliability to prevent work-related musculoskeletal disorders (WMSDs).
Background: Since risk assessment methods such as OWAS and REBA are relied on visual observations, their accuracy and reliability might have been in questions. Therefore, computer-aided risk assessment systems are necessary to enhance accuracy and reliability as well as to improve applicability in workplaces.
Method: This study developed vector-based OWAS (V-OWAS) and deep learning-based OWAS (DL-OWAS). V-OWAS (partial automation) asks a user to manually identify the key joint positions from a working picture and automatically calculates OWAS scores and action level. DL-OWAS (high automation) automatically identifies the key positions and determines OWAS scores and action level. To evaluate the performance of the two systems, we used 40 working pictures and compared the results of the two systems with the results of four ergonomic experts with over 10-year experience who did assess based on traditional visual observations.
Results: The two systems showed similar assessment results with those obtained from the ergonomic experts. The agreement percentage of V-OWAS (91.3%) with the experts was slightly better than DL-OWAS (90%). On the other hand, the usability score of DL-OWAS (7 out of 7 point Likert scale) was significantly higher than V-OWAS (5.75) and traditional visual observation (5.25).
Conclusion: V-OWAS had an advantage in accuracy of identifying the key joint positions; but it required more time and efforts over DL-OWAS. DL-OWAS can assess automatically; but its accuracy might be low if some parts of the body are obscured due to some reasons (e.g., obscured by some machine). Therefore, it is recommended to use V-OWAS or DL-OWAS according to the condition of working picture.
Application: The two OWAS systems developed in this study can help industrial practitioners effectively and efficiently assess working posture for preventing WMSDs.
Background: Since risk assessment methods such as OWAS and REBA are relied on visual observations, their accuracy and reliability might have been in questions. Therefore, computer-aided risk assessment systems are necessary to enhance accuracy and reliability as well as to improve applicability in workplaces.
Method: This study developed vector-based OWAS (V-OWAS) and deep learning-based OWAS (DL-OWAS). V-OWAS (partial automation) asks a user to manually identify the key joint positions from a working picture and automatically calculates OWAS scores and action level. DL-OWAS (high automation) automatically identifies the key positions and determines OWAS scores and action level. To evaluate the performance of the two systems, we used 40 working pictures and compared the results of the two systems with the results of four ergonomic experts with over 10-year experience who did assess based on traditional visual observations.
Results: The two systems showed similar assessment results with those obtained from the ergonomic experts. The agreement percentage of V-OWAS (91.3%) with the experts was slightly better than DL-OWAS (90%). On the other hand, the usability score of DL-OWAS (7 out of 7 point Likert scale) was significantly higher than V-OWAS (5.75) and traditional visual observation (5.25).
Conclusion: V-OWAS had an advantage in accuracy of identifying the key joint positions; but it required more time and efforts over DL-OWAS. DL-OWAS can assess automatically; but its accuracy might be low if some parts of the body are obscured due to some reasons (e.g., obscured by some machine). Therefore, it is recommended to use V-OWAS or DL-OWAS according to the condition of working picture.
Application: The two OWAS systems developed in this study can help industrial practitioners effectively and efficiently assess working posture for preventing WMSDs.
목차
1. Introduction
2. Development of V-OWAS System
3. Development of DL-OWAS System
4. Performance Evaluation of the Developed Systems
5. Discussion and Conclusion
References
참고문헌 (25)
참고문헌 신청
Buchholz, B. / 1996 / PATH: A work sampling-based approach to ergonomic job analysis for construction and other non-repetitive work / Applied Ergonomics 27(3):177~187
Cha, J. H. / 2018 / A Study on 2D Human Pose Estimation Techniques / Journal of the Korea Information Processing Society 25(2):811~812
Cheon, W. / 2020 / Comparison of evaluation results between ergonomic experts and novices for OWAS, RULA, and REBA to improve effectiveness of risk factors assessment / Journal of Korea Society Management and Science 22(2):31~28
He, K. / 2016 / Deep Residual Learning for Image Recognition / IEEE Conference on Computer Vision and Pattern Recognition
임수정 / 2011 / 작업관련성 근골격계질환의 인간공학적 평가 기법들에 대한 사용성 평가 / 대한안전경영과학회지 13(2):83~90
Cha, J. H. / 2018 / A Study on 2D Human Pose Estimation Techniques / Journal of the Korea Information Processing Society 25(2):811~812
Cheon, W. / 2020 / Comparison of evaluation results between ergonomic experts and novices for OWAS, RULA, and REBA to improve effectiveness of risk factors assessment / Journal of Korea Society Management and Science 22(2):31~28
He, K. / 2016 / Deep Residual Learning for Image Recognition / IEEE Conference on Computer Vision and Pattern Recognition
임수정 / 2011 / 작업관련성 근골격계질환의 인간공학적 평가 기법들에 대한 사용성 평가 / 대한안전경영과학회지 13(2):83~90
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