Human Motion Mode Recognition Based on Multi-parameter Fusion of Wearable Inertial Module Unit and Flexible Pressure Sensor

Aiming at the rapid recognition of human motion modes required in the intelligent control algorithm of exoskeleton robots, in this paper, on the basis of the characteristics of inertial data and pressure data collected by smart terminals carried by pedestrians, a dynamic block matching algorithm based on kinematics (DBMK) using motion mode recognition is proposed. This algorithm involves signal extraction and motion feature matching discrimination. More specifically, it first uses the method of periodic signal capture in adaptive motion mode to capture the heel touch event from the signal collected by a flexible pressure sensor mounted on the heel, and extracts the corresponding periodic signal. Finally, the DBMK algorithm uses a self-made lower limb motion information acquisition system to obtain human motion angle data. After kinematics preprocessing, the distance correlation coefficient based on Pearson weight proposed in this paper is used to identify the current human motion model category. The DBMK algorithm was used to identify five common human motion modes from the output data of inertial module units and flexible pressure sensors, and experimental results show that the proposed DBMK algorithm has an accuracy of 90.86% for the recognition of the five common motion modes.