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
- 2014.6
- 수록면
- 21 - 28 (8page)
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초록· 키워드
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Objectives: Existing methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology.
Methods: A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset.
Results: Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates.
Conclusions: Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.
Methods: A large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset.
Results: Error-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates.
Conclusions: Our results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.
목차
Abstract
Introduction
Material and methods
Results
Discussion and conclusions
References
참고문헌 (30)
참고문헌 신청
National Research Council and the Institute of Medicine / 2011 / Musculoskeletal Disorders and the Workplace: Low Back and Upper Extremities / Panel on Musculoskeletal Disorders and the Workplace. Commission on Behavioral and Social Sciences and Education
Dutta T / 2012 / Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace / Appl Ergon 43:645~649
Silverstein BA / 2008 / Rotator cuff syndrome: Personal, work-related psychosocial and physical load factors / J Occup Environ Med 50:1062~1076
Fan ZJ / 2009 / Quantitative exposure-response relations between physical workload and prevalence of lateral epicondylitis in a working population / Am J Ind Med 52:479~490
Bao S / 2009 / Interrater reliability of posture observations / Hum Factors 51:292~309
Dutta T / 2012 / Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace / Appl Ergon 43:645~649
Silverstein BA / 2008 / Rotator cuff syndrome: Personal, work-related psychosocial and physical load factors / J Occup Environ Med 50:1062~1076
Fan ZJ / 2009 / Quantitative exposure-response relations between physical workload and prevalence of lateral epicondylitis in a working population / Am J Ind Med 52:479~490
Bao S / 2009 / Interrater reliability of posture observations / Hum Factors 51:292~309
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UCI(KEPA) : I410-ECN-0101-2016-517-001953169