EMG-based Real Time Facial Gesture Recognition for Stress Monitoring

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).

Multi‐Electrode Printed Bioelectronic Patches for Long‐Term Electrophysiological Monitoring

“…Moreover, the possibility of recording muscle activity in the face (and specifically near the jaw), has relevance for monitoring orthodontia-related conditions such as bruxism or temporomandibular joint disorder, as discussed in Ref. [69] differential acquisition channels and placed on the proximal portion of the anterior right forearm of the user and a ground electrode that lays in the user's elbow. As a sanity check, a handgrip dynamometer (Figure 8B) was used to measure the relation between EMG intensity and gripping strength.…”

Stress Monitoring Device and its Future Direction

“…It can monitor a person's stress levels by recording their facial expressions as they sleep to detect specific patterns in certain motions (for instance the act of clenching by the masticatory muscles). 30 The EMG signal is used because of its efficiency and the measurement of the muscle electrical activity is displayed as a function of time in terms of frequency, amplitude, and phase. 31 The action potential of the EMG signals, shows higher amplitude when the muscles are exposed to stressors or stressful events compared to resting.…”

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

“…Natural habits, such as facial expressions are the reflection of the psychological state and the indication of emotion that a person is experiencing. Lots of researchers are trying to capture these subtle-signs and correlate them with stress situations through facial electromyography (EMG) [ 64 ] or image recognition based on facial expression [ 65 ].…”