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Excessive low back joint loading during material handling tasks is considered a critical risk factor of musculoskeletal disorders (MSD). Therefore, it is necessary to understand the low-back joint loading during manual material handling to prevent low-back injuries. Recently, computer vision-based pose reconstruction methods have shown the potential in human kinematics and kinetics analysis. This study performed L5/S1 joint moment estimation by combining VideoPose3D, an open-source pose reconstruction library, and a biomechanical model. Twelve participants lifting a 10 kg plastic crate from the floor to a knuckle-height shelf were captured by a camera and a laboratory-based motion tracking system. The L5/S1 joint moments obtained from the camera video were compared with those obtained from the motion tracking system. The comparison results indicate that estimated total peak L5/S1 moments during lifting tasks were positively correlated to the reference L5/S1 joint moment, and the percentage error is 7.7%.
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A mobile platform app to assist learning human kinematics in undergraduate biomechanics courses
Biomechanics examines different physical characteristics of the human body movement by applying principles of Newtonian mechanics to physical activities. Therefore, undergraduate biomechanics courses are highly demanding in mathematics and physics. While the inclusion of laboratory experiences can augment student comprehension of biomechanics concepts, the cost and the required expertise associated with motion tracking systems can be a burden of offering laboratory sessions. In this study, we developed a mobile platform app to facilitate learning human kinematics in biomechanics courses. An optimized computer-vision model that is based on convolutional pose machine (CPM), MobileNet V2 and TensorFlow Lite frameworks is adopted to reconstruct human pose first. A real-time human kinematics analysis then allows students to conduct human motion experiments. The proposed app can serve as a potential instructional tool in biomechanics courses.
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- Award ID(s):
- 2013451
- PAR ID:
- 10417918
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 66
- Issue:
- 1
- ISSN:
- 2169-5067
- Page Range / eLocation ID:
- 475 to 479
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1071181322661058
