Recognition of Turkish Sign Language (TID) Using sEMG Sensor

Mustafa Seddiqi, Hasan Kivrak, Hatice Kose

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

4 Citations (Scopus)

Abstract

Using computer-based recognition of hand gestures can enhance interaction between humans and computers. In this paper, the recognition using surface electromyography (sEMG) of stationary hand signs of Turkish Sign Language (TID) is evaluated. We used an armband to obtain sEMG signals and a sliding window-based method to split the data for extracting features. We obtained sEMG and inertial measurement unit (IMU) data from ten subjects (five male and five female) for a selected subset of stationary TID signs. We pre-processed the signals, extracted time-domain and time-frequency-domain features from the sEMG signals. The signs are analyzed for their classification performance with sEMG signals. A random forest classifier for five TID signs is trained on the dataset and achieved 78% leave-one-subject-out cross-validation accuracy for the male subjects. The difference in sEMG signals of males and females is analyzed. The performance of time-domain and time-frequency domain features of sEMG, and the inertial measurement unit (IMU) on the hand gesture recognition are evaluated.

Original languageEnglish
Title of host publicationProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728191362
DOIs
Publication statusPublished - 15 Oct 2020
Externally publishedYes
Event2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 - Istanbul, Turkey
Duration: 15 Oct 202017 Oct 2020

Publication series

NameProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020

Conference

Conference2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020
Country/TerritoryTurkey
CityIstanbul
Period15/10/2017/10/20

Keywords

  • Gesture Recognition
  • Random Forest Classifier
  • Sign Language
  • surface EMG

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