Sensor based dynamic hand gesture recognition by pairnet

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

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

2 Citations (Scopus)

Abstract

This paper presents a novel feedforward neural network for sensor-based dynamic hand gesture recognition. The algorithm, termed PairNet, is capable of carrying out accurate gesture spotting for the sensory data produced by basic accelerators and gyroscopes, which are commonly deployed in internet of things devices. The gesture classification outcomes are then obtained from the spotting results by the Maximum A Posteriori (MAP) estimation. To illustrate the effectiveness of the proposed algorithm, a prototype system based on a mobile phone has been implemented. Experimental results reveal that, while attaining realtime operations, the proposed algorithm has superior accuracy over existing sensor-based counterparts for hand gesture recognition.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Congress on Cybermatics
Subtitle of host publication12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages994-1001
Number of pages8
ISBN (Electronic)9781728129808
DOIs
Publication statusPublished - 2019 Jul
Event12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 - Atlanta, United States
Duration: 2019 Jul 142019 Jul 17

Publication series

NameProceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019

Conference

Conference12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
CountryUnited States
CityAtlanta
Period19/7/1419/7/17

Keywords

  • Continuous hand gesture recognition
  • Convolutional neural networks
  • Human machine interface

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Communication

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  • Cite this

    Jhang, Y. J., Chu, Y. C., Tai, T. M., Hwang, W. J., Cheng, P. W., & Lee, C. K. (2019). Sensor based dynamic hand gesture recognition by pairnet. In Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 (pp. 994-1001). [8875280] (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00174