Lower-Gait Tracking Mobile Application: A Case Study of Lower body Motion Capture Comparison Between Vicon T40 System and Apple Augmented Reality

Abstract

Tracking the motions and positions in three-dimensional space is an exciting human gait analysis approach for healthcare and clinical examination. The high-end motion capture systems, such as Vicon cameras, could automate the gait analysis process, but the system is too costly for a clinical setting. We developed a cost-effective motion capture and gait analysis system using an existing commercial camera found on a mobile device. We take advantage of the Artificial Intelligence and Machine Learning technique in mobile devices for human pose estimation and detection to keep track of body motion in the environment. This paper presents our mobile application case study to measure three-dimensional kinematics of lower-body gait of the hip, knee, and ankle during walking tasks using the Apple Augmented reality toolkit. We evaluated our application by comparing it with the measurements observed by a Vicon T40s motion-tracking system. The results showed that the gait movement can be measured in real-time. The lower-gait angles of hip and knee agreed with those from the Vicon system, whereas the motions of small joint sections were not captured as accurately. The proposed application is a cost-effective, easy-to-use, and mobile gait analysis system that can be used to construct three-dimensional gait scores to improve the examination of gait and decision making in various clinical populations.