EMG-based Real Time Facial Gesture Recognition for Stress Monitoring
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embc_orguc.pdf
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4.16 MB
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Author(s)
Orguc, Sirma
Khurana, Harneet Singh
Stankovic, Konstantina M.
Leel, H.S.
Chandrakasan, Anantha P
Date Issued
July 2018
Journal
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
IEEE
Citation
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
Version
Author's final manuscript
Abstract
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).
MIT Department
Massachusetts Institute of Technology. Microsystems Technology Laboratories
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/embc.2018.8512781
