title: A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors.
abstract: 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: Jian
authors: J
authors: Wu
authors: Lu
authors: L
authors: Sun
authors: Roozbeh
authors: R
authors: Jafari
mesh_terms: D001132:Arm / Q000502:physiology; D003143:Communication Devices for People with Disabilities; D004576:Electromyography / Q000379:methods*; D004581:Electronics; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012813:Sign Language; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D049250:Speech Recognition Software