
Open access
Date
2017-07Type
- Conference Paper
Citations
Cited 21 times in
Web of Science
Cited 30 times in
Scopus
ETH Bibliography
yes
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Abstract
To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000228392Publication status
publishedExternal links
Book title
2017 International Conference on Rehabilitation Robotics (ICORR)Pages / Article No.
Publisher
IEEEEvent
Subject
Electromyography; Myo armband; real-time gesture classification; wearable robotic hand orthosisOrganisational unit
03827 - Gassert, Roger / Gassert, Roger
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Show all metadata
Citations
Cited 21 times in
Web of Science
Cited 30 times in
Scopus
ETH Bibliography
yes
Altmetrics