A Lightweight Motion Function Analysis System Based on Binocular Vision

Gait analysis is significant in clinical and rehabilitation medicine for evaluating lower limb diseases. However, the existing gait measurement and analysis technologies rely on large-scale professional equipment and physicians, and their application scenarios are limited. In this work, we design a lightweight motor function analysis system, including multi-sensor camera and gait extraction - assessment algorithm. The system first uses binocular active infrared sensors to collect information about the subjects and build a body model. Then, the binocular red-green-blue (RGB) camera is used to collect the subject’s motion information, and the established subject model is used to identify and fit the human movement posture and extract the gait features. We build a convolutional neural network to process gait features and evaluate the disease status of subjects. The effectiveness of the system is verified by experiments. We recruit healthy subjects and patients to validate the system and achieve an accuracy of 92.86% on the disease assessment task, verifying the effectiveness of the system.