Lower Body Rehabilitation Dataset and Model Optimization

Chenxi Wang, Ying Li, Zinan Xiong, Yan Luo, Yu Cao

Abstract: Human pose estimation has enabled numerous applications by classifying and tracking body movements. Although a few open datasets have emerged to facilitate the evaluation of pose detection methods, they are too generic to benefit do-main specific applications such as physical therapy which has quantitative clinical metrics and requires precise differentiation and measurement. To address this issue, we construct the first human keypoints detection dataset for physical therapy, in particular lower body rehabilitation. The dataset consists of 1,885,637 distinctive human poses for 31 lower body rehab exercises, which are performed by 20 actors under the guidance of a licensed physical therapist. Their motion are captured with state of art of motion tracking system in both 3D and 2D to establish the ground truth. Furthermore, to optimize a number of deep learning models applied on this unique dataset, we utilize Extremely Efficient Spatial Pyramid (EESP) and attention mechanism to reduce the models’ computational complexity. Our experiment results show that the optimized models achieve comparable performance with nearly 4x reduction in complexity.

External IDs:dblp:conf/icmcs/WangLXL021