A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors. Academic Article uri icon


  • A sign language recognition system translates signs performed by deaf individuals into text/speech in real time. Inertial measurement unit and surface electromyography (sEMG) are both useful modalities to detect hand/arm gestures. They are able to capture signs and the fusion of these two complementary sensor modalities will enhance system performance. In this paper, a wearable system for recognizing American Sign Language (ASL) in real time is proposed, fusing information from an inertial sensor and sEMG sensors. An information gain-based feature selection scheme is used to select the best subset of features from a broad range of well-established features. Four popular classification algorithms are evaluated for 80 commonly used ASL signs on four subjects. The experimental results show 96.16% and 85.24% average accuracies for intra-subject and intra-subject cross session evaluation, respectively, with the selected feature subset and a support vector machine classifier. The significance of adding sEMG for ASL recognition is explored and the best channel of sEMG is highlighted.

published proceedings

  • IEEE J Biomed Health Inform

altmetric score

  • 6

author list (cited authors)

  • Wu, J., Sun, L. u., & Jafari, R.

citation count

  • 160

complete list of authors

  • Wu, Jian||Sun, Lu||Jafari, Roozbeh

publication date

  • September 2016