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Sensors 2016, 16(11), 1782; doi:10.3390/s16111782

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

1
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
2
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
3
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 17 July 2016 / Revised: 18 October 2016 / Accepted: 21 October 2016 / Published: 25 October 2016
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
View Full-Text   |   Download PDF [865 KB, uploaded 26 October 2016]   |  

Abstract

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. View Full-Text
Keywords: surface electromyography; Gaussian mixture models; hidden Markov models; machine learning; pattern recognition surface electromyography; Gaussian mixture models; hidden Markov models; machine learning; pattern recognition
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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

Siu, H.C.; Shah, J.A.; Stirling, L.A. Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography. Sensors 2016, 16, 1782.

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