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Design of a Wearable Smart sEMG Recorder Integrated Gradient Boosting Decision Tree based Hand Gesture Recognition.

Authors
  • Song, Wei
  • Wang, Anhe
  • Chen, Yang
  • Bai, Shuo
  • Han, Qingquan
  • Lin, Zhonghang
  • Yan, Nan
  • Luo, Deng
  • Liao, Yiqiao
  • Zhang, Milin
  • Wang, Zhihua
  • Xie, Xiang
Type
Published Article
Journal
IEEE Transactions on Biomedical Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Nov 18, 2019
Identifiers
DOI: 10.1109/TBCAS.2019.2953998
PMID: 31751286
Source
Medline
Language
English
License
Unknown

Abstract

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition. A hydrogen-silica gel based flexible surface electrode band is used as the tissue interface. The sEMG signal is collected using a neural signal acquisition analog front end (AFE) chip. A quantitative analysis method is proposed to balance the algorithm complexity and recognition accuracy. A parallel GBDT implementation is proposed featuring a low latency. The proposed GBDT based neural signal processing unit (NSPU) is implemented on an FPGA near the AFE. A RF module is used for wireless communication. A hand gesture set including 12 gestures is designed for human-computer interaction. Experimental results show an overall hand gesture recognition accuracy of 91%.

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