随机传感器仿生手的肌电信号采集与手部姿势分类

Sumit A. Raurale, J. McAllister, J. M. D. Rincón
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引用次数: 17

摘要

提出了一种独特的用于肌电信号采集和分类的实时运动识别系统。这是第一个可以从随机放置的手臂传感器收集的多通道肌电信号中准确地分类手部姿势的方法。它结合了时域特征提取、线性判别分析(LDA)特征投影和多层感知器(MLP)分类,允许超过95%的时间正确识别9种不同的姿势。这可与最先进的放置式传感器肌电信号采集系统相媲美。11.70 ms的处理时间也使其成为在实际假肢应用中实时肌电采集和处理的可行候选方法。
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Emg Acquisition and Hand Pose Classification for Bionic Hands from Randomly-Placed Sensors
This paper presents a unique real-time motion recognition system for Electromyographic (EMG) signal acquisition and classification. It is the first approach which can classify hand poses from multi-channel EMG signals gathered from randomly placed arm sensors as accurately as current placed-sensor EMG acquisition approaches. It combines time-domain feature extraction, Linear Discriminant Analysis (LDA) feature projection and Multilayer Perceptron (MLP) classification to allow nine distinct poses to be correctly identified more than 95% of the time. This is comparable to state-of-the-art placed-sensor EMG acquisition systems. Processing times of 11.70 ms also make this a viable candidate approach for real-time EMG acquisition and processing in practical prosthesis applications.
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