Lower Body Rehabilitation Dataset and Model Optimization

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 domain 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.

Swin-Pose: Swin Transformer Based Human Pose Estimation

“…* Equal contribution Recently, deep convolutional neural networks (DCNNs) has proved its ability in computer vision tasks. Among all these approaches, two of them are mainstream methods: keypoint position regression [2,3], and keypoint heatmap estimation followed by choosing the location with the highest score [4,5,6]. The former one treats pose estimation as a joint position regression problem and regress the locations of each joint keypoint directly.…”