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
- 2020.10
- 수록면
- 511 - 528 (18page)
- DOI
- 10.5143/JESK.2020.39.5.511
이용수
이 논문의 연구 히스토리 (7)
2022
2021
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초록· 키워드
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Objective: The purposes of this study were to revise the AWBA (Agricultural Whole-Body Assessment) considering disc compression according to various workloads and compare Re-AWBA with existing posture risk assessment tools (RULA, REBA, OWAS, and AWBA).
Background: Due to the nature of the agricultural site, workers have been frequently exposed to musculoskeletal disorders risk according to awkward working postures. AWBA was developed to overcome the limitations of existing posture risk assessment tools in evaluating such agricultural work postures. AWBA consisted of selecting upper-limb posture and lower-limb posture from AULA (Agricultural Whole-Body Assessment) and ALLA (Agricultural Lower-Limb Assessment). Since AWBA did not consider the workload, there was a limitation in assessing workloads and weights.
Method: The modification of AWBA was conducted based on the compression force of the lumbar disc computed by 3D-SSPP software. According to working type (onehanded and two-handed) and weight (0, 2, 4, 6, 8, and 10kg), the lumbar disc compression force was used to subdivide the current risk levels of AWBA. K-means analysis was used to subdivide each level, and the number of sub-levels was decided by the Nbclust package in Rstudio. For comparison between tools, the posture risk of 30 agricultural works was evaluated according to the height of the working point.
Results: Based on the current level of AWBA, level 1 and level 2 were divided into three sub-levels, and level 3 and level 4 were divided into four. A total of 11 tables were newly created according to the existing AWBA"s posture combination and workload type. As a result of comparing the posture risk assessment results according to the height of the working point, AWBA and Revised AWBA evaluated higher than other tools at the lower working point.
Conclusion: Revised AWBA was modified by subdividing the existing AWBA results based on the lumbar compression force only. Therefore, in further studies, Re-AWBA would be improved by collecting various biomechanical data according to each posture and workload.
Application: Revised AWBA could be used more effectively than existing tools for evaluating the risk in agricultural work. It is believed that it would help eliminate the risk factors that exist in agricultural sites.
Background: Due to the nature of the agricultural site, workers have been frequently exposed to musculoskeletal disorders risk according to awkward working postures. AWBA was developed to overcome the limitations of existing posture risk assessment tools in evaluating such agricultural work postures. AWBA consisted of selecting upper-limb posture and lower-limb posture from AULA (Agricultural Whole-Body Assessment) and ALLA (Agricultural Lower-Limb Assessment). Since AWBA did not consider the workload, there was a limitation in assessing workloads and weights.
Method: The modification of AWBA was conducted based on the compression force of the lumbar disc computed by 3D-SSPP software. According to working type (onehanded and two-handed) and weight (0, 2, 4, 6, 8, and 10kg), the lumbar disc compression force was used to subdivide the current risk levels of AWBA. K-means analysis was used to subdivide each level, and the number of sub-levels was decided by the Nbclust package in Rstudio. For comparison between tools, the posture risk of 30 agricultural works was evaluated according to the height of the working point.
Results: Based on the current level of AWBA, level 1 and level 2 were divided into three sub-levels, and level 3 and level 4 were divided into four. A total of 11 tables were newly created according to the existing AWBA"s posture combination and workload type. As a result of comparing the posture risk assessment results according to the height of the working point, AWBA and Revised AWBA evaluated higher than other tools at the lower working point.
Conclusion: Revised AWBA was modified by subdividing the existing AWBA results based on the lumbar compression force only. Therefore, in further studies, Re-AWBA would be improved by collecting various biomechanical data according to each posture and workload.
Application: Revised AWBA could be used more effectively than existing tools for evaluating the risk in agricultural work. It is believed that it would help eliminate the risk factors that exist in agricultural sites.
목차
1. Introduction
2. Methods
3. Results
4. Discussion and Conclusions
5. Conclusions
References
참고문헌 (23)
참고문헌 신청
Abbas, O. A. / 2007 / Comparisons between data clustering algorithms / The International Arab Journal of Information Technology 5(3):320~325
Callaghan, J. P. / 2001 / Low back joint loading and kinematics during standing and unsupported sitting / Ergonomics 44(3):280~294
Charrad, M. / 2014 / NbClust : An R package for determining the relevant number of clusters in a data set / Journal of Statistical Software 61(6):1~36
Dimitriadou, E. / 2002 / An examination of indexes for determining the number of clusters in binary data sets / Psychometrika 67(1):137~159
Ferguson, S. A. / 2002 / Spinal loading when lifting from industrial storage bins / Ergonomics 45(6):399~414
Callaghan, J. P. / 2001 / Low back joint loading and kinematics during standing and unsupported sitting / Ergonomics 44(3):280~294
Charrad, M. / 2014 / NbClust : An R package for determining the relevant number of clusters in a data set / Journal of Statistical Software 61(6):1~36
Dimitriadou, E. / 2002 / An examination of indexes for determining the number of clusters in binary data sets / Psychometrika 67(1):137~159
Ferguson, S. A. / 2002 / Spinal loading when lifting from industrial storage bins / Ergonomics 45(6):399~414
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UCI(KEPA) : I410-ECN-0101-2020-530-001591594