Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 20, Issue 5, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2016.2598302
Keywords
American Sign Language (ASL) recognition; feature selection; inertial measurement unit (IMU) sensor; sensor fusion; surface EMG (sEMG)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available