Random Forest Classification of Finger Movements using Electromyogram (EMG) Signals

被引：0
|
作者：
Findik, Mucahit [1 ]
Yilmaz, Seyma [1 ]
Koseoglu, Mehmet [1 ]
机构：
[1] Hacettepe Univ, Dept Comp Engn, TR-06800 Ankara, Turkey
来源：
2020 IEEE SENSORS | 2020年
关键词：
electromyogram signals; classification; machine learning; feature selection;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
One of the fundamental problems in the development of prosthetic fingers is the recognition of finger movements using surface electrocardiogram (EMG) data. Most of the previous studies have proposed the classification of EMG signals using features curated using expert knowledge. We here consider automatic generation and selection of EMG signal features without relying on domain knowledge. We then develop a classification method based on random forests. Our results show that the proposed method achieves 97.5% accuracy. We also present a discussion of the features which are important in distinguishing finger movements.
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页数：4
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