Transformation classification of human squat/sit-to-stand based on multichannel information fusion

Abstract

In existing rehabilitation training, research on the accuracy of recognizing completed actions has achieved good results; however, the reduction in the misjudgment rate in the action conversion process needs further research. This article proposes a multichannel information fusion method for the movement conversion process of squat/sit-to-stand, which can help online movement conversion classification during rehabilitation training. We collected a training dataset from a total of eight subjects performing three different motions, including half squat, full squat, and sitting, equipped with plantar pressure sensors, RGB cameras, and five inertial measurement units. Our evaluation includes the misjudgment rate for each action and the time needed for classification. The experimental results show that, compared with the recognition of a single sensor, the accuracy after fusion can reach 96.6% in the case of no occlusion and 86.7% in the case of occlusion. Compared with the complete time window, the classification time window is shortened by approximately 25%.