基于高动态力感电阻的步态识别研究

Peng Yang, Xiaodong Cai, Yanli Geng, Lingling Chen
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引用次数: 0

摘要

人的步态包含了下肢运动姿态的信息。步态识别与分析可以为下肢康复机器人提供控制信息。在不同的步态阶段,足部接触地面的位置是不同的。因此,可以根据足底压力进行步态分类。本研究探讨了人类下肢行走过程中足底压力信息的变化。首先,利用STM32单片机采集高动态力敏电阻(HD-FSR)检测的足底压力信息;其次,将采集到的信息通过蓝牙传输到上位机;最后,对步态各阶段压力特征进行了分析和分类。针对支持向量机(SVM)分类算法中两个常量参数对分类效果影响较大的问题,提出了一种基于粒子群优化(PSO)的支持向量机分类算法,实现对不同阶段的识别。
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The Research of Gait Recognition Based on High Dynamic Force Sensing Resistor
The human gait contains the information of lower limb movement posture. Gait recognition and analysis can provide control information for lower limb rehabilitation robots. In different gait phases, the position of the foot touching the ground is different. Therefore, gait classification can be performed according to plantar pressure. This study investigated the changes in plantar pressure information during human lower limb walking. Firstly, the STM32 single-chip microcomputer is used to collect the plantar pressure information detected by the high dynamic force sensing resistor(HD-FSR); secondly, the collected information is transmitted to the upper computer through Bluetooth; finally, all kinds of gait phases pressure features are analyzed and classified. Since the two constant parameters in the Support Vector Machine(SVM) classification algorithm have a great influence on the classification effect, a SVM classification algorithm based on the Particle Swarm Optimization(PSO) is proposed to realize the recognition of different phases.
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