一种改进的基于表面肌电信号的HMI中减少肌肉疲劳影响的增量在线训练算法

2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2012-12-11 DOI:10.1109/ROBIO.2012.6491046

Yi Zhang, Xinli Xu, Yuan Luo, Huosheng Hu, Huiyu Zhou

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引用次数: 2

摘要

针对基于表面肌电信号(sEMG)的人机界面(HMI)在肌肉疲劳过程中稳定性逐渐下降的问题，提出了一种新的样本更新方法，以改进支持向量机(SVM)的增量在线训练算法。采用基于连续小波变换的方法研究肌肉疲劳时表面肌电信号的变化，并将改进的增量在线支持向量机应用于表面肌电信号分类。实验结果表明，该算法能显著提高分类准确率和训练速度。此外，该方法有效地减少了肌电图人机界面长期操作时肌肉疲劳的影响。

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An improved incremental online training algorithm for reducing the influence of muscle fatigue in sEMG based HMI

Considering the problem that stability of surface Electromyographic Signal (sEMG) based human-machine interface (HMI) gradually declines as fatigue takes place in muscles, we propose a novel method for updating samples to improve incremental online training algorithm for support vector machine (SVM). We study the changes of sEMG when muscle fatigue occurs using a method based on continuous wavelet transform, and then applies the improved incremental online SVM for sEMG classification. Experiment results show that the proposed algorithm can be used to improve the classification accuracy and training speed significantly. Furthermore, this method effectively diminish the influence of muscle fatigue during long-term operation of sEMG based HMI.

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2012 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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