EMG-based Real Time Facial Gesture Recognition for Stress Monitoring
An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an ampl...
| Main Authors: | , , , , |
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| Format: | Article |
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IEEE
2020
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| Online Access: | https://hdl.handle.net/1721.1/123872 |
| _version_ | 1811072782767751168 |
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| author | Orguc, Sirma Khurana, Harneet Singh Stankovic, Konstantina M. Leel, H.S. Chandrakasan, Anantha P |
| author2 | Massachusetts Institute of Technology. Microsystems Technology Laboratories |
| author_facet | Massachusetts Institute of Technology. Microsystems Technology Laboratories Orguc, Sirma Khurana, Harneet Singh Stankovic, Konstantina M. Leel, H.S. Chandrakasan, Anantha P |
| author_sort | Orguc, Sirma |
| collection | MIT |
| description | An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an amplifier and an ADC, implemented in 65nm CMOS technology, has been used for signal acquisition [1]. It consumes 3.8nW power from a 0.3V battery. Feature extraction and classification is performed in software every 300ms to give real-time feedback to the user. Discrete wavelet transforms (DWT) are used for feature extraction in the time-frequency domain. The dimensionality of the feature vector is reduced by selecting specific wavelet decomposition levels without compromising the accuracy, which reduces the computation cost of feature extraction in embedded implementations. A support vector machine (SVM) is used for the classification. Overall, the system is capable of identifying several jaw movements such as clenching, opening the jaw and resting in real-time from a single channel EMG data, which makes the system suitable for providing biofeedback during sleeping and awake states for stress monitoring, bruxism, and several orthodontic applications such as temporomandibular joint disorder (TMJD). |
| first_indexed | 2024-09-23T09:12:40Z |
| format | Article |
| id | mit-1721.1/123872 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:12:40Z |
| publishDate | 2020 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1238722022-09-30T14:03:50Z EMG-based Real Time Facial Gesture Recognition for Stress Monitoring Orguc, Sirma Khurana, Harneet Singh Stankovic, Konstantina M. Leel, H.S. Chandrakasan, Anantha P Massachusetts Institute of Technology. Microsystems Technology Laboratories An electromyogram (EMG) signal acquisition system capable of real time classification of several facial gestures is presented. The training data consist of the facial EMG collected from 10 individuals (5 female/5 male). A custom-designed sensor interface integrated circuit (IC) consisting of an amplifier and an ADC, implemented in 65nm CMOS technology, has been used for signal acquisition [1]. It consumes 3.8nW power from a 0.3V battery. Feature extraction and classification is performed in software every 300ms to give real-time feedback to the user. Discrete wavelet transforms (DWT) are used for feature extraction in the time-frequency domain. The dimensionality of the feature vector is reduced by selecting specific wavelet decomposition levels without compromising the accuracy, which reduces the computation cost of feature extraction in embedded implementations. A support vector machine (SVM) is used for the classification. Overall, the system is capable of identifying several jaw movements such as clenching, opening the jaw and resting in real-time from a single channel EMG data, which makes the system suitable for providing biofeedback during sleeping and awake states for stress monitoring, bruxism, and several orthodontic applications such as temporomandibular joint disorder (TMJD). 2020-02-27T18:50:55Z 2020-02-27T18:50:55Z 2018-07 Article http://purl.org/eprint/type/ConferencePaper 9781538636466 https://hdl.handle.net/1721.1/123872 S. Orguc, H. S. Khurana, K. M. Stankovic, H. S. Leel and A. P. Chandrakasan, "EMG-based Real Time Facial Gesture Recognition for Stress Monitoring," 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 2651-2654 http://dx.doi.org/10.1109/embc.2018.8512781 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE Prof. Chandrakasan |
| spellingShingle | Orguc, Sirma Khurana, Harneet Singh Stankovic, Konstantina M. Leel, H.S. Chandrakasan, Anantha P EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title | EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title_full | EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title_fullStr | EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title_full_unstemmed | EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title_short | EMG-based Real Time Facial Gesture Recognition for Stress Monitoring |
| title_sort | emg based real time facial gesture recognition for stress monitoring |
| url | https://hdl.handle.net/1721.1/123872 |
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