sEMG_DeepLearning:基于sEMG的深度学习手势识别

上传者: 42107374 | 上传时间: 2021-06-23 16:01:19 | 文件大小: 12.45MB | 文件类型: ZIP
基于表面肌电信号的动作识别(深度学习) 1、sEMG的基础知识 1-1 sEMG的产生 表面肌电信号是由多个运动单元发放的动作电位序列,在皮肤表面呈现的时间上和空间上综合叠加的结果。 图1 肌电信号生成 sEMG的特点: 幅值一般和肌肉运动力度成正比,能精确的反映肌肉自主收缩力 超前于人体运动30-150ms产生 1-2 基于sEMG的动作识别一般处理流程 图2 基于机器学习的肌电识别处理流程 (1)离线采集sEMG 定义动作数量、动作类型 选择采集设备:Delsys(2000Hz)、Myo(200Hz)、OttoBock(100Hz)、高密度阵列式等 肌肉位置的选择、电极数量的选择:根据肌肉解剖位置调整电极 引导方式:图片、语音 采集流程:休息+动作循环采集 休息时间、动作时间,动作维持的力的大小,动作的姿势尽量保持一致 (2)数据预处理 10-350Hz带通滤波器,50Hz陷波器

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