Live Demonstration: Home Appliance Control System with Dynamic Hand Gesture Recognition base on 3D Hand Skeletons

Tsung Han Tsai, Yi Jhen Luo, Wei Chung Wan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present a two-stage lightweight convolutional neural network architecture on hand gesture recognition for home appliance control system. At the first stage, we utilize DetNet to detect the hand and generate 3D hand skeleton locations. At the second stage, a skeleton-based dynamic hand gesture recognition model is developed. We have 99.4% accuracy by the trained CNN model with our testing dataset. Besides, we implement this system on the Nvidia Jetson AGX Xavier to control the on/off of the fan and the light and the overall system achieve 15 fps.

Original languageEnglish
Title of host publicationProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages503
Number of pages1
ISBN (Electronic)9781665409964
DOIs
StatePublished - 2022
Event4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of
Duration: 13 Jun 202215 Jun 2022

Publication series

NameProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022

Conference

Conference4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Country/TerritoryKorea, Republic of
CityIncheon
Period13/06/2215/06/22

Keywords

  • convolutional neural networks
  • dynamic hand gesture recognition
  • home appliance application
  • skeleton

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