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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-330
ISBN (Electronic)9781457702204
Publication statusPublished - Aug 2016

Publication series

Name
ISSN (Print)1557-170X

Conference

Title38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
LocationDisney's Contemporary Resort
PlaceUnited States
CityOrlando
Period16 - 20 August 2016

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.

Citation Format(s)

Short latency hand movement classification based on surface EMG spectrogram with PCA. / Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H. M.; Tin, Chung.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Institute of Electrical and Electronics Engineers Inc., 2016. p. 327-330 7590706.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review