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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
出版社Institute of Electrical and Electronics Engineers Inc.
ページ256-258
ページ数3
ISBN(電子版)9781665419048
DOI
出版ステータスPublished - 2022
イベント4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, Japan
継続期間: 2022 3月 72022 3月 9

出版物シリーズ

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

Conference

Conference4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
国/地域Japan
CityOsaka
Period22/3/722/3/9

ASJC Scopus subject areas

  • 農業および生物科学(その他)
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 生体医工学
  • 器械工学
  • 教育

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