Recognition of hand gesture sequences by accelerometers and gyroscopes

Yen Cheng Chu, Yun Jie Jhang, Tsung Ming Tai, Wen Jyi Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by accelerometers and gyroscopes. They are the extensions from the PairNet, which is a Convolutional Neural Network (CNN) capable of carrying out simple pairing operations with low computational complexities. Three different types of feedforward NNs, termed Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are proposed for the extension. They are the PairNet operating in conjunction with short-cut connections and/or inception modules for achieving high classification accuracy and low computation complexity. A prototype system based on smart phones for remote control of home appliances has been implemented for the performance evaluation. Experimental results reveal that the PairNet has superior classification accuracy over its basic CNN and Recurrent NN (RNN) counterparts. Furthermore, the Residual PairNet, PairNet with Inception, and Residual PairNet with Inception are able to further improve classification hit rate and/or reduce recognition time for hand gesture recognition.

Original languageEnglish
Article number6507
JournalApplied Sciences (Switzerland)
Volume10
Issue number18
DOIs
Publication statusPublished - 2020 Sep
Externally publishedYes

Keywords

  • Artificial intelligence
  • Feedforward neural networks
  • Hand gesture recognition
  • Human-machine interface

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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