A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors
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 complement...
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| Published in | IEEE journal of biomedical and health informatics Vol. 20; no. 5; pp. 1281 - 1290 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Jafari, Roozbeh Jian Wu Lu Sun |
| Author_xml | – sequence: 1 surname: Jian Wu fullname: Jian Wu email: jian.wu@tamu.edu organization: Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA – sequence: 2 surname: Lu Sun fullname: Lu Sun email: lsun@traxid.com organization: Univ. of Texas at Dallas, Richardson, TX, USA – sequence: 3 givenname: Roozbeh surname: Jafari fullname: Jafari, Roozbeh email: rjafari@tamu.edu organization: Depts. of Biomed. Eng., Texas A&M Univ., College Station, TX, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27576269$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Accelerometers American Sign Language (ASL) recognition Arm - physiology Assistive technology Communication Aids for Disabled Electromyography - methods Electronics Equipment Design Feature extraction feature selection Female Gesture recognition Humans inertial measurement unit (IMU) sensor Male Muscle, Skeletal - physiology Pattern Recognition, Automated - methods Real-time systems sensor fusion Sensor systems Sign Language Signal Processing, Computer-Assisted - instrumentation Speech Recognition Software surface EMG (sEMG) |
| Title | A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors |
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