Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks

Anmol Khanna; Senthil Arumugam Muthukumaraswamy

doi:10.1109/ICECA.2017.8212835

Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks

Anmol Khanna, Senthil Arumugam Muthukumaraswamy

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.

Original language	English
Title of host publication	2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)
Publisher	IEEE
Pages	366-371
Number of pages	6
ISBN (Electronic)	9781509056866
ISBN (Print)	9781509056859
DOIs	https://doi.org/10.1109/ICECA.2017.8212835
Publication status	Published - 18 Dec 2017

Keywords

Arduino
Cost-Effective
Electromyography
Muscular Intent
Neural Network
Orthosis
Prosthesis

ASJC Scopus subject areas

Computer Networks and Communications
Aerospace Engineering
Electrical and Electronic Engineering
Instrumentation
Safety, Risk, Reliability and Quality

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10.1109/ICECA.2017.8212835

Cite this

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title = "Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks",

abstract = "This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3\% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.",

keywords = "Arduino, Cost-Effective, Electromyography, Muscular Intent, Neural Network, Orthosis, Prosthesis",

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Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks. / Khanna, Anmol; Muthukumaraswamy, Senthil Arumugam.
2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. p. 366-371.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Muthukumaraswamy, Senthil Arumugam

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Y1 - 2017/12/18

N2 - This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.

AB - This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition and decoding of muscular intent. This result is indicative of the potential of the Arduino microcontroller in this application, to provide effective performance at a far lower price-point than its competition.

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KW - Cost-Effective

KW - Electromyography

KW - Muscular Intent

KW - Neural Network

KW - Orthosis

KW - Prosthesis

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BT - 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

PB - IEEE

ER -

Cost-effective system for the classification of muscular intent using surface electromyography and artificial neural networks

Abstract

Keywords

ASJC Scopus subject areas

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