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Improving gesture recognition by bidirectional temporal convolutional networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Surface electromyography (sEMG) based gesture recognition as an important role in Muscle-Computer interface has been researched for decades. Recently, deep learning based method has had a profound impact on this field. CNN, RNN and RNN-CNN based methods were studied by many researchers. Motivated by Bidirectional Long short-term memory (Bi-LSTM) and Temporal Convolutional Networks (TCN), we propose 1D CNN based networks called Bidirectional Temporal Convolutional Networks (Bi-TCN). The positive order signal and reverse order sEMG signal are feed to our networks to learn the different representation of the same sEMG signal. We evaluate proposed networks on two benchmark datasets, Ninapro DB1 and DB5. Our networks yields 90.74% prediction accuracy on DB1 and 90.06% prediction accuracy on DB5. The results demonstrate our networks is comparable to the state-of-the-art works.
Original languageEnglish
Title of host publicationICRRI 2020: Robotics and Rehabilitation Intelligence
Subtitle of host publicationFirst International Conference, ICRRI 2020, Fushun, China, September 9–11, 2020, Proceedings, Part II
EditorsJianhua Qian, Honghai Liu, Jiangtao Cao, Dalin Zhou
PublisherSpringer
Pages413-424
Number of pages12
ISBN (Electronic)978-981-33-4932-2
ISBN (Print)978-981-33-4931-5
DOIs
Publication statusPublished - 3 Jan 2021
Event1st International Conference on Robotics and Rehabilitation Intelligence - Fushun, China
Duration: 9 Sep 202011 Sep 2020

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Singapore
Volume1336
ISSN (Print)1865-0929

Conference

Conference1st International Conference on Robotics and Rehabilitation Intelligence
Abbreviated titleICRRI 2020
CountryChina
CityFushun
Period9/09/2011/09/20

Documents

  • ICRRI2020_219_final_v2-2

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Qian J., Liu H., Cao J., Zhou D. (eds) Robotics and Rehabilitation Intelligence. ICRRI 2020. Communications in Computer and Information Science, vol 1336. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-981-33-4932-2_30.

    Accepted author manuscript (Post-print), 778 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 18/12/21

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