Recognition of Japanese Sign Language by Sensor-Based Data Glove Employing Machine Learning

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

Abstract

This paper presents a sensor-based data acquisition glove for gesture recognition in Japanese Sign Language (JSL), which uses five flex sensors and an inertial measurement unit (IMU) to detect finger flexion and hand motion information. The detected data is sent from the Arduino Micro to a computer. We collected data from the "A"to "Ta"lines of the Japanese (kana) syllabary and using four different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) to recognize them. RF and KNN have the highest average accuracy, reaching 99.75%. Also, SVM and DT had an average accuracy of 99% and 94.25% respectively. The experimental results show that the proposed system has great potential for gesture recognition in Japanese Sign Language.

Original languageEnglish
Title of host publicationLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-258
Number of pages3
ISBN (Electronic)9781665419048
DOIs
Publication statusPublished - 2022
Event4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, Japan
Duration: 2022 Mar 72022 Mar 9

Publication series

NameLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies

Conference

Conference4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Country/TerritoryJapan
CityOsaka
Period22/3/722/3/9

Keywords

  • Japanese Sign Language Recognition
  • Machine Learning
  • Sensor

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Instrumentation
  • Education

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