Sign language estimation scheme employing Wi-Fi signal

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

Abstract

The sign language recognition system plays an important role in the field of human-computer interaction. In the daily life of hearing-impaired people, sign language is used as the main tool to communicate with the world. Although sign language can satisfy simple conversation, it is difficult to deal with in some situations where a lot of conversation is required such as medical emergencies or educational consultation. This paper proposes a sign language recognition system based on Wi-Fi to improve the life of the disabled The proposed system collects the Channel State Information (CSI) due to the change of hand movement. Through the analysis of all subcarriers, the amplitude of CSI is determined to reflect the characteristics of different sign languages, some high-frequency noise is removed in the amplitude of CSI to obtain a smoother signal Gesture feature. We propose a gesture feature extraction method based on the variance of time series and DTW algorithm is used to recognize nine common Japanese sign language gestures. We set two daily conditions to test the system, and the experimental results show that the system performs well in different conditions.

Original languageEnglish
Title of host publication2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194318
DOIs
Publication statusPublished - 2021 Aug 23
Event2021 IEEE Sensors Applications Symposium, SAS 2021 - Virtual, Sundsvall, Sweden
Duration: 2021 Aug 232021 Aug 25

Publication series

Name2021 IEEE Sensors Applications Symposium, SAS 2021 - Proceedings

Conference

Conference2021 IEEE Sensors Applications Symposium, SAS 2021
Country/TerritorySweden
CityVirtual, Sundsvall
Period21/8/2321/8/25

Keywords

  • CSI
  • Device-free
  • Gesture recognition
  • Japanese sign language
  • Wi-Fi

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Instrumentation

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