On the performance of surface electromyography-based onset detection methods with real data in assistive technologies Comparative analysis and enhancements via sensor fusion

Assistive Technologies are used to increase the autonomy of people with motor disabilities by enhancing their functional capabilities. Surface Electromyography sensors have been explored in the scope of Assistive Technologies as an input modality, to provide greater control and flexibility. In this...

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Vydáno v:Multimedia tools and applications Ročník 77; číslo 9; s. 11491 - 11520
Hlavní autoři: Reis, Margarida, Almeida, Carlos, Rocha, Rui M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2018
Témata:
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Multimedia Information Systems
Special Purpose and Application-Based Systems
Embedded systems
Onset detection
Accelerometer
Assistive technologies
Sensor fusion
Surface electromyography
Human–computer interaction
ISSN:1380-7501, 1573-7721
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Shrnutí:Assistive Technologies are used to increase the autonomy of people with motor disabilities by enhancing their functional capabilities. Surface Electromyography sensors have been explored in the scope of Assistive Technologies as an input modality, to provide greater control and flexibility. In this case, triggering signals are dependent on the detection of the moment when the user performs a voluntary muscular contraction. In the literature, various methods to determine this onset have been studied, but mainly for the non-disabled population and may not be designed to deal with the low signal-to-noise ratio, motion artifacts and spasms, frequently observed in people with motor disabilities, which may trigger false positives. The main purpose of this article is to perform a comparative analysis of different methods in multiple configurations of their parameters, with the goal of selecting one that can be implemented in an embedded system, targeting real time and wireless operation of a tool for Human-Computer Interaction. Furthermore, in this work we seek to improve the performance of existing onset detection methods, through a proposed sensor fusion approach, combining an Accelerometer with the Surface Electromyography sensor, to integrate motion analysis in the process of validating or rejecting muscle events.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-017-4963-8