Knowledge transfer-driven estimation of knee moments and ground reaction forces from smartphone videos via temporal-spatial modeling of augmented joint kinematics
Md Sanzid Bin Hossain, Hwan Choi, Zhishan Guo, Sunyong Yoo, Min-Keun Song, Hyunjun Shin, Dexter Hadley, Anne Martin, Anne Martin, Anne Martin
TL;DR
This paper presents a method to estimate knee joint moments and ground reaction forces using smartphone videos, offering a cost-effective alternative to traditional equipment.
Contribution
The study introduces a novel approach combining knowledge transfer, joint kinematics augmentation, and temporal-spatial modeling for accurate video-based biomechanical estimation.
Findings
The student model reduced NRMSE from 5.71 to 4.68 using knowledge transfer.
The PCC increased from 0.929 to 0.951 with the proposed method.
Temporal-spatial modeling and multi-modal fusion improved smartphone video-based estimation accuracy.
Abstract
The knee adduction and flexion moment provides critical information about knee joint health, while 3D ground reaction forces (GRFs) help identify force and energy characteristics for maneuvering the entire human body. Existing methods of acquiring joint moments and GRFs require expensive equipment, time-consuming pre-processing, and limited accessibility. This study proposes to tackle these limitations by utilizing only smartphone videos to estimate joint moments and 3D GRFs accurately. We also propose the augmentation of joint kinematics by generating additional modalities of 2D joint center velocity and acceleration from 2D joint center position acquired from the videos. This augmented joint kinematics helps to apply a multi-modal fusion module to learn the importance of inter-modal interactions. Additionally, we utilize recurrent neural networks and graph convolutional networks to…
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Taxonomy
TopicsBalance, Gait, and Falls Prevention · Human Pose and Action Recognition · Sports Performance and Training