Regression convolutional neural network for improved simultaneous EMG control.

Journal: Journal of neural engineering

PMID: 30849774

Abstract

OBJECTIVE: Deep learning models can learn representations of data that extract useful information in order to perform prediction without feature engineering. In this paper, an electromyography (EMG) control scheme with a regression convolutional neural network (CNN) is proposed as a substitute of conventional regression models that use purposefully designed features.

Authors

Ali Ameri

Department of Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad Ali Akhaee

School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Erik Scheme

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada.
Kevin Englehart

Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, Canada.

Keywords

Adult Electromyography Female Humans Machine Learning Male Motion Movement Neural Networks, Computer Random Allocation Regression Analysis

External Resources

View on PubMed Access via DOI PubMed (30849774)

Regression convolutional neural network for improved simultaneous EMG control.

Abstract

Authors

Keywords

External Resources

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