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Technologies 2017, 5(4), 64; doi:10.3390/technologies5040064

Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses

1
Idiap Research Institute, Rue Marconi 19, 1920 Martigny, Switzerland
2
DLR—German Aerospace Center, Münchener Str. 20, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Received: 15 August 2017 / Revised: 29 September 2017 / Accepted: 2 October 2017 / Published: 6 October 2017
(This article belongs to the Special Issue Assistive Robotics)
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Abstract

Despite recent advances in prosthetics and assistive robotics in general, robust simultaneous and proportional control of dexterous prosthetic devices remains an unsolved problem, mainly because of inadequate sensorization. In this paper, we study the application of regression to muscle activity, detected using a flexible tactile sensor recording muscle bulging in the forearm (tactile myography—TMG). The sensor is made of 320 highly sensitive cells organized in an array forming a bracelet. We propose the use of Gaussian process regression to improve the prediction of wrist, hand and single-finger activation, using TMG, surface electromyography (sEMG; the traditional approach in the field), and a combination of the two. We prove the effectiveness of the approach for different levels of activations in a real-time goal-reaching experiment using tactile data. Furthermore, we performed a batch comparison between the different forms of sensorization, using a Gaussian process with different kernel distances. View Full-Text
Keywords: prosthetic hands; surface electromyography; tactile myography; multimodal regression; Gaussian processes; assistive robotics prosthetic hands; surface electromyography; tactile myography; multimodal regression; Gaussian processes; assistive robotics
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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. (CC BY 4.0).

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MDPI and ACS Style

Jaquier, N.; Connan, M.; Castellini, C.; Calinon, S. Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses. Technologies 2017, 5, 64.

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