基于ARM的肌电假肢手控制器

Design of Electromyography Prosthesis Controller Based on ARM

由于肌电假肢手大多基于阈值的张、合控制,并存在操作灵活性差等问题,提出一种基于ARM的肌电假肢手控制器设计方案.采用ARM核STM32处理器作为主控芯片,通过2路A/D采集手臂尺侧腕屈肌和桡侧腕屈肌的肌电信号,分别提取时域和频域上的4种特征值,并采用BP神经网络分类算法实现对5种手掌动作模式的在线实时识别.实验结果表明,该控制器对5种动作的整体在线识别率可达97%,且符合实时性要求,很好地满足了残疾人假肢手控制的需求.

As most electromyography prosthesis controller systems are based on a threshold to control hand＇s opening and closing, with poor operational flexibility, it is proposed to use ARM in the system. A STM32 ARM core processor is used as the main chip. It collects the flxor carpi ulnaris and flexor carpi radialis electomyography signals with 2 A/D signal converters, and extractes 4 kinds of characteristic values both in the time and frequency domains. By using a BP neural network classification algorithm, the system realizes real-time online identification of 5 kinds of palm action modes. Experimental results show that the system＇s online recognition rate for the 5 actions is up to 97%, meeting the real-time requirements of prosthetic hand control.