Short latency hand movement classification based on surface EMG spectrogram with PCA

Xiaolong Zhai; Beth Jelfs; Rosa H.M. Chan; Chung Tin

doi:10.1109/EMBC.2016.7590706

Short latency hand movement classification based on surface EMG spectrogram with PCA

Xiaolong Zhai, Beth Jelfs, Rosa H.M. Chan, Chung Tin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Citations (Scopus)

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
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	https://doi.org/10.1109/EMBC.2016.7590706
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
Volume	2016-October
ISSN (Print)	1557-170X

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
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

Access to Document

10.1109/EMBC.2016.7590706

Cite this

Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2016). Short latency hand movement classification based on surface EMG spectrogram with PCA. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 327-330). Article 7590706 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EMBC.2016.7590706

Zhai, Xiaolong ; Jelfs, Beth ; Chan, Rosa H.M. et al. / Short latency hand movement classification based on surface EMG spectrogram with PCA. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 327-330 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Short latency hand movement classification based on surface EMG spectrogram with PCA",

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.",

author = "Xiaolong Zhai and Beth Jelfs and Chan, {Rosa H.M.} and Chung Tin",

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Zhai, X, Jelfs, B, Chan, RHM & Tin, C 2016, Short latency hand movement classification based on surface EMG spectrogram with PCA. in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016., 7590706, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2016-October, Institute of Electrical and Electronics Engineers (IEEE), pp. 327-330, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/08/16. https://doi.org/10.1109/EMBC.2016.7590706

Short latency hand movement classification based on surface EMG spectrogram with PCA. / Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H.M. et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 327-330 7590706 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Zhai X, Jelfs B, Chan RHM, Tin C. Short latency hand movement classification based on surface EMG spectrogram with PCA. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 327-330. 7590706. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2016.7590706

Short latency hand movement classification based on surface EMG spectrogram with PCA

Abstract

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Bibliographical note

ASJC Scopus subject areas

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