基于肌电信号的智能轮椅控制

R. Mahendran
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引用次数: 14

摘要

提出了一种利用肌电信号控制智能轮椅的人工神经网络方法。研究分为六个阶段,其中特征提取和分类是本研究的主要阶段。使用的分类技术类型是多层感知器。肌电图数据是通过在前臂肌肉上放置电极来收集的。该数据每200毫秒进行一次分割,之后使用平均值进行特征提取。这些信号被送入人工神经网络并进行处理以获得与手部肌肉时间活动相关的参数。生成的命令被发送到驱动轮椅根据用户的意图。该软件在智能轮椅上进行了实时测试,验证了该系统对不同性别和环境的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EMG signal based control of an intelligent wheelchair
This paper presents a novel artificial neural network approach to control an intelligent wheelchair using myoelectric signals. The work is divided into six stages out of which feature extraction and classification are the main stages for this research. The type of classification technique used is Multi-Layer Perceptron. The EMG data is collected by placing the electrodes on the forearm muscles. This data is segmented for every 200 milliseconds after which the feature extraction is performed using mean absolute value. The signals are fed to the artificial neural networks and processed to attain parameters that are related to the muscles temporal hand activities. The resulting commands are sent to drive the wheelchair according to the user's intention. The software was tested on the intelligent wheelchair in real-time, which confirm that the system is robust for different gender and environments.
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