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A Projection-Based Human Motion Recognition Algorithm Based on Depth Sensors
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DOI：10.1109/JSEN.2021.3079983.png)
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Abstract
En 中文
Exercise monitoring systems for rehabilitation are usually not able to pinpoint the exact part for patients' exercise. The research objective is to develop the projection-based motion recognition (PMR) algorithm based on depth data and wide-accepted methods to solve this matter. We regard a motion trajectory as a combination of basic posture units, and then project the basic posture units onto a 2-D space via a projection mapping. Each motion trajectory is transformed to a 2-D motion trajectory map by sequentially connecting the basic posture units involved in the motion trajectory. Finally, we employ a convolutional neural network (CNN)-based classifier to classify the trajectory maps. Accurate classification rate reaches as high as 95.21%. The originality of PMR algorithm lies in (1) it has the generalization capability to some extent since it only adopts popular methods and contains an essential and comprehensive mechanism; (2) the resultant trajectory map may reveal the information about how well a patient execute the rehabilitation assignments.
Keywords:
Trajectory
Sensors
Monitoring
Clustering algorithms
Image recognition
Hidden Markov models
Oceans
Motion trajectory
spatial-temporal pattern recognition
therapeutic exercise
deep learning
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