A computer vision based method for 3D posture estimation of symmetrical lifting

• Published in: Journal of biomechanics Vol. 69; pp. 40 - 46
• Main Authors: Mehrizi, Rahil, Peng, Xi, Xu, Xu, Zhang, Shaoting, Metaxas, Dimitris, Li, Kang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2018; Elsevier Limited
• Subjects: Algorithms; Biomechanical Phenomena; Biomechanics; Camcorders; Cameras; Computer vision; Discriminative approach; Female; Histograms; Hoisting; Human mechanics; Human motion; Humans; Joint kinematics assessment; Joints - physiology; Kinematics; Lifting; Male; Marker-less motion capture; Materials handling; Methods; Middle Aged; Motion capture; Movement; Musculoskeletal diseases; Pattern recognition; Photography; Posture; Studies; Surface markers; Workers; Computer vision; Discriminative approach; Marker-less motion capture; Lifting; Joint kinematics assessment
• ISSN: 0021-9290; 1873-2380; 1873-2380
• DOI: 10.1016/j.jbiomech.2018.01.012

Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.