A Projection-Based Human Motion Recognition Algorithm Based on Depth Sensors

Abstract

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.