Next Article in Journal
Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
Next Article in Special Issue
Assistant without Master? Some Conceptual Implications of Assistive Robotics in Health Care
Previous Article in Journal
The Non-Euclidean Hydrodynamic Klein–Gordon Equation with Perturbative Self-Interacting Field
Article

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.
Technologies 2017, 5(4), 64; https://doi.org/10.3390/technologies5040064
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)
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
Show Figures

Figure 1

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. https://doi.org/10.3390/technologies5040064

AMA 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(4):64. https://doi.org/10.3390/technologies5040064

Chicago/Turabian Style

Jaquier, Noémie, Mathilde Connan, Claudio Castellini, and Sylvain Calinon. 2017. "Combining Electromyography and Tactile Myography to Improve Hand and Wrist Activity Detection in Prostheses" Technologies 5, no. 4: 64. https://doi.org/10.3390/technologies5040064

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop