Vision-based human joint angular velocity estimation during squat and walking on a treadmill actions.

Abstract

Elderly health monitoring, rehabilitation training, and sport supervision could benefit from continuous assessment of joint angle, and angular velocity to identify the joint movement patterns. However, most of the measurement systems are designed based on special kinematic sensors to estimate angular velocities. The study aims to measure the lower limb joint angular velocity based on a 2D vision camera system during squat and walking on treadmill action using deep convolution neural network (CNN) architecture. Experiments were conducted on 12 healthy adults, and six digital cameras were used to capture the videos of the participant actions in lateral and frontal view. The normalized cross-correlation (Ccnorm) analysis was performed to obtain a degree of symmetry of the ground truth and estimated angular velocity waveform patterns. Mean Ccnorm for angular velocity estimation by deep CNN model has higher than 0.90 in walking on the treadmill and 0.89 in squat action. Furthermore, joint-wise angular velocities at the hip, knee, and ankle joints were observed and compared. The proposed system gets higher estimation performance under the lateral view and the frontal view of the camera. This study potentially eliminates the requirement of wearable sensors and proves the applicability of using video-based system to measure joint angular velocities during squat and walking on a treadmill actions.