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A mobile platform app to assist learning human kinematics in undergraduate biomechanics courses
DescriptionBiomechanics 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 in 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.