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Journal of Sensors
Volume 2017 (2017), Article ID 3980906, 12 pages
https://doi.org/10.1155/2017/3980906
Research Article

An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices

1Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan
2Department of Electronic and Physical Systems, Waseda University, Tokyo, Japan

Correspondence should be addressed to Masayuki Yokoyama; pj.ca.adesaw.sc.balsi@amayokoy.ikuyasam

Received 27 April 2017; Revised 27 July 2017; Accepted 1 October 2017; Published 25 October 2017

Academic Editor: Ji Zhang

Copyright © 2017 Masayuki Yokoyama et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Hand-force prediction is an important technology for hand-oriented user interface systems. Specifically, surface electromyography (sEMG) is a promising technique for hand-force prediction, which requires a sensor with a small design space and low hardware costs. In this study, we applied several artificial neural-network (ANN) regression models with different numbers of neurons and hidden layers and evaluated handgrip forces by using a dynamometer. A handwear with dry electrodes on the dorsal interosseous muscles was used for our evaluation. Eleven healthy subjects participated in our experiments. sEMG signals with six different levels of forces from 0 N to 200 N and maximum voluntary contraction (MVC) are measured to train and test our ANN regression models. We evaluated three different methods (intrasession, intrasubject, and intersubject evaluation), and our experimental results show a high correlation (0.840, 0.770, and 0.789 each) between the predicted forces and observed forces, which are normalized by the MVC for each subject. Our results also reveal that ANNs with deeper layers of up to four hidden layers show fewer errors in intrasession and intrasubject evaluations.