Jayashree L. S., Madhana K., Supreeth K. S.
Human gait analysis provides qualitative and quantitative information regarding the characteristics of walking of a given subject. Cost-efficient RGB-D cameras can estimate the 3D position of several body joints without the use of markers. The acquired information can be used to perform objective gait and injury analysis for sports people such as basketball, volleyball, handball, etc., in an affordable and portable way. Amateur or professional athletes have a high tendency of suffering Anterior Cruciate Ligament (ACL) injuries. On the other hand, the design of smart healthcare techniques is paving the way with the increase of Internet capabilities and advanced sensors. The integration of cloud, IoT, and edge become an important area of research to meet the time-critical requirements of smart healthcare services. In this contribution, a real-time gait monitoring system based on edge computing is presented for automatic gait analysis and knee injury analysis using a Microsoft Kinect camera. An algorithm to estimate the heel-strike events during a gait cycle, aiding in the measurement of spatiotemporal gait parameters is implemented. Few studies suggest that low flexion angle and high valgus angle tend to increase the strain on ACL. Using the proposed Kinect-based motion capture system, it should be possible to determine knee injuries due to valgus knee location by studying the Knee abduction angle and the Knee-Ankle separation ratio (KASR) in some gestures simulating dynamic movements of the jump oriented ball games. 3D kinematic algorithms were developed using Microsoft Kinect V2 environment to calculate lower limb joint angles for some sports activities. The results confirm the reliability of the Kinect apparatus for gait analysis and its analyzing capability of knee injury risks in professional ball games.
Kinematics, Motion Capture Systems, varus-valgus, Anterior Cruciate Ligament, Edge Computing, Cloud, Internet of
Cite this paper
Jayashree L. S., Madhana K., Supreeth K. S.. (2022) A Real-time Gait Monitoring System based on Edge Computing using Motion Capture Techniques for Knee Injury Analysis. International Journal of Biology and Biomedicine, 7, 44-55