TY - GEN
T1 - Recognition of Japanese Sign Language by Sensor-Based Data Glove Employing Machine Learning
AU - Ji, Li
AU - Liu, Jiang
AU - Shimamoto, Shigeru
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Japanese Sign Language Recognition
KW - Machine Learning
KW - Sensor
UR - http://www.scopus.com/inward/record.url?scp=85129172931&partnerID=8YFLogxK
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U2 - 10.1109/LifeTech53646.2022.9754851
DO - 10.1109/LifeTech53646.2022.9754851
M3 - Conference contribution
AN - SCOPUS:85129172931
T3 - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
SP - 256
EP - 258
BT - LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Y2 - 7 March 2022 through 9 March 2022
ER -