Data-driven phase extraction for anomaly detection of repetitive human movements by Jatesiktat, Prayook

“…An evaluation on four different exercises shows good potential in separating anomalous movements from normal ones with an average area under the curve (AUC) of 0.9872 and the test on actual stroke survivors is also performed. In addition, a new real-time depth-based object tracking method is invented by training a neural network to learn a continuous signed distance field of a target object. …”
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