Time-Varying Motion Pattern Detection with Application in Coaching and Rehabilitation
MS Kinect premiere has introduced new possibilities in the field of motion capture and has inspired many researchers to use it in coaching or rehabilitation support systems. Nonetheless, the majority of researches have been focused on game development and do not emphasize on motion analysis. In this thesis a set of tools are provided to detect certain motion pattern for rehabilitation, coaching or other similar area. A novel set of tracking signals, originated from joints data of body movement, along with their selection algorithm is proposed. The signals are utilized by a novel time-varying motion pattern detection algorithm which operates in the time domain and only needs one sample of a training pattern. The performance of the algorithm is evaluated on a group of five people performing seven types of exercises 10 times each, giving 350 samples. The performance evaluation shows significant success of the proposed algorithm. Also in spite of low recall factors, the results promise the high potential of future use of the algorithm. Finally, an interactive software application was created to record movement, create the reference pattern and perform coaching of individual movements.
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