On the performance of surface electromyography-based onset detection methods with real data in assistive technologies


  • Margarida Reis, Instituto Superior Técnico, Universidade de Lisboa, Portugal
  • Rui M. Rocha, Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Portugal
  • Carlos Almeida, Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Portugal

Source: Multimedia Tools and Applications: Special Issue on Multimedia Tools for Physiological Computing, 2017



Assistive Technologies are used to increase the autonomy of people with motor disabilities by enhancing their functional capabilities. Surface Electromyography sensorsn 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.


Accelerometer · Assistive technologies · Embedded systems · Human–computer interaction · Sensor fusion · Surface electromyography · Onset detection


© Springer Science+Business Media, LLC 2017

Full Text: The final publication is available at https://link.springer.com/article/10.1007/s11042-017-4963-8 .