Electromyography and Speech Controlled Prototype Robotic Car using CNN Based Classifier for EMG

Zahid Ul Hassan, Nouman Bashir, Afaq Iltaf
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Abstract

Wearable electronic equipment is continually improving and becoming more integrated with technology for prosthesis control. These devices, which come in a variety of shapes and sizes, can detect, quantify, and perhaps use signals generated by the human body's physiological and muscular changes to control machinery. One such gadget, the MYO gesture/arm band, collects information from our forearm in the form of electromyographic (EMG) Signal, which is based on the measurement of small electrical impulses caused by ion exchange between muscle membranes, utilize these myoelectric impulses and converts them into input signals by using pre-defined motions. There is a range of tasks that may be carried out with this device and use of this device can give better results in a combination with another controlling modality. This paper addresses the use of several input modalities, including speech and myoelectric signals recorded through microphone and MYO band, respectively to control a robotic car. Hand gestures are used to control the car through MYO armband. The complete process is done by using Raspberry Pi. Classification of EMG signals is done by using Convolution Neural Network (CNN) classifier. Experimental results obtained as well as their accuracies for performance analysis are also presented.
基于CNN分类器的肌电图与语音控制原型机器人车
可穿戴电子设备正在不断改进,并与假肢控制技术越来越融合。这些设备有各种形状和大小,可以检测、量化,也许还可以利用人体生理和肌肉变化产生的信号来控制机器。一个这样的小工具,MYO手势/臂带,以肌电图(EMG)信号的形式从我们的前臂收集信息,这是基于测量肌肉膜之间离子交换引起的小电脉冲,利用这些肌电脉冲,并通过使用预先定义的动作将其转换为输入信号。使用该装置可执行一系列任务,并且与另一种控制方式结合使用该装置可获得更好的结果。本文介绍了几种输入方式的使用,包括通过麦克风和MYO波段记录的语音和肌电信号,分别用于控制机器人汽车。手势是用来控制汽车通过MYO臂章。完整的过程是通过使用树莓派完成的。采用卷积神经网络(CNN)分类器对肌电信号进行分类。并给出了实验结果及其性能分析的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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