An Investigation of Dimensionality Reduction Techniques for EMG-based Force Estimation

被引：0

作者：

Hajian, Gelareh ^{[1
]}

Etemad, Ali ^{[1
]}

Morin, Evelyn ^{[1
]}

机构：

[1] Queens Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada

来源：

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2019年

关键词：

Surface electromyogram; High-density surface EMG; principle component analysis; t-distributed stochastic neighbor embedding; and artificial neural network;

D O I：

10.1109/embc.2019.8856293

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

In this paper, extracted features in time and frequency domain, from high-density surface electromyogram (HD-sEMG) signals acquired from the long head and short head of biceps brachii, and brachioradialis during isometric elbow flexion are used to estimate force induced at the wrist using an artificial neural network (ANN). Different hidden layer sizes were considered to investigate its effect on the model accuracy. Also, we applied two dimensionality reduction techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), on the feature set and investigated their effects on force estimation accuracy.

引用

页码：698 / 701

页数：4

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