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An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices
Joint Authors
Yokoyama, Masayuki
Koyama, Ryohei
Yanagisawa, Masao
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yokoyama, Masayuki& Koyama, Ryohei& Yanagisawa, Masao. 2017. An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices. Journal of Sensors،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1186940
Modern Language Association (MLA)
Yokoyama, Masayuki…[et al.]. An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices. Journal of Sensors No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1186940
American Medical Association (AMA)
Yokoyama, Masayuki& Koyama, Ryohei& Yanagisawa, Masao. An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1186940
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186940