Abstract
Postural assessment is crucial in the sports screening system to reduce the risk of severe injury. The capture of the athlete’s posture using computer vision attracts huge attention in the sports community due to its markerless motion capture and less interference in the physical training. In this paper, a novel markerless gait estimation and tracking algorithm is proposed to locate human key-points in spatial-temporal sequences for gait analysis. First, human pose estimation using OpenPose network to detect 14 core key-points from the human body. The ratio of body joints is normalized with neck-to-pelvis distance to obtain camera invariant key-points. These key-points are subsequently used to generate a spatial-temporal sequences and it is fed into Long-Short-Term-Memory network for gait recognition. An indexed person is tracked for quick local pose estimation and postural analysis. This proposed algorithm can automate the capture of human joints for postural assessment to analyze the human motion. The proposed system is implemented on Intel Up Squared Board and it can achieve up to 9 frames-per-second with 95% accuracy of gait recognition.
Highlights
Postural assessments using computer vision have received substantial attention in the sports community for sports screening and injury prevention [3, 6]
The statistic reported in [13] shows that 30 million people suffer from osteoarthritis (OA) in the United State, of whom more than half are under age 65
To achieve the low computational cost, this paper proposes a markerless gait estimation and tracking for postural assessment with edge computing to do the sports screening
Summary
Postural assessments using computer vision have received substantial attention in the sports community for sports screening and injury prevention [3, 6]. Markerless-based motion analysis approach tracks the boundaries or features of human bodies without the mounted markers using camera It retrieves the spatialtemporal dimension of salient objects such as human skeleton and motion [9, 22, 24, 34] in the video. Since it does not require the attachment of sensors and markers, the quality of kinematic data can be improved in the physical training for precise postural assessment in the sports field [26]. The extension of this research can cover other areas of sports such as weight lifting, diving and cycling
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