Abstract
Hand gesture recognition from forearm surface electromyography (sEMG) is an active research field in the development of motor prosthesis. Studies have shown that classification accuracy and efficiency is highly dependent on the features extracted from the EMG. In this paper, we show that EMG spectrograms are a particularly effective feature for discriminating multiple classes of hand gesture when subjected to principal component analysis for dimensionality reduction. We tested our method on the Ninapro database which includes sEMG data (12 channels) of 40 subjects performing 50 different hand movements. Our results demonstrate improved classification accuracy (by ∼10%) over purely time domain features for 50 different hand movements, including small finger movements and different levels of force exertion. Our method has also reduced the error rate (by ∼12%) at the transition phase of gestures which could improve robustness of gesture recognition when continuous classification from sEMG is required.
Original language | English |
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Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 327-330 |
Number of pages | 4 |
ISBN (Electronic) | 9781457702204 |
DOIs | |
Publication status | Published - 13 Oct 2016 |
Externally published | Yes |
Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016 |
Publication series
Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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Volume | 2016-October |
ISSN (Print) | 1557-170X |
Conference
Conference | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
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Country/Territory | United States |
City | Orlando |
Period | 16/08/16 → 20/08/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics