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In recent years, electrooculography (EOG) has become a well-established method used in studies related to neuroscience, cognition analysis, psychological behavior, assistive technology, and sleep. The traditional placement of the EOG electrodes around the eyes tends to cause discomfort and is prone to displacement during sleep. This article presents a more comfortable and reliable way to measure the sleep EOG signals: a wireless smart mouthguard with integrated EOG sensors. The smart mouthguard is made of two ethylene-vinyl acetate (EVA) submouthguards, five conductive fabric electrodes, a flexible EOG measuring board, and a small LiPo battery, which is encapsulated in the Ecoflex for waterproofing. The device can transfer the EOG data through Bluetooth Low Energy (BLE) 5.0 to an Android app or computer terminal and can be charged wirelessly. The magnitude of skin-electrode impedance of the fabric electrodes on the mouthguard is less than 14 k $\Omega $ , which is comparable to that of the standard gold EOG electrodes. The EOG measurement board has the differential gain over 19 (or 25.6 dB) and common-mode rejection ratio (CMRR) over 98.6 dB in the EOG bandwidth. The smart EOG mouthguard is validated on eight subjects for detecting EOG signal patterns of different eye activities. Results show that different eye activities can be detected from the acquired EOG signal with an accuracy of 100% for horizontal eye movements and at least 94% for vertical eye movements. As the smart mouthguard fits well and do not get displaced during sleep, the proposed smart mouthguard has potential for comfortable long-term sleep EOG monitoring.

In recent years, wearable health monitoring using photoplethysmogram (PPG) has become a popular trend. However, one of the main challenges of PPG sensing is the power usage of the light-emitting diode (LED) in the technique. In this work, we designed a wireless wearable finger patch that can record PPG signals solely using ambient light, entirely eliminating the requirement for LED power consumption. The finger patch is implemented on a two-layer flexible polyimide substrate and is based on a high sensitivity silicon photodiode (PD) and a high dynamic range analog front end (AFE). The entire circuit is powered by a rechargeable Li-ion coin battery. It also uses a Bluetooth module to send the PPG data wirelessly. The system is validated with 12 healthy subjects for the collection of PPG signals under three ambient light conditions. Assessment of the PPG signals quality demonstrates that the PPG signals acquired under the ambient light conditions can be placed in the reliable or acceptable category. Finally, two vital signs: heart rate (HR) and blood pressure (BP) are extracted from the no-LED mode PPG signals obtained from 12 subjects using the finger patch. The calculated maximum mean absolute error (MAE) for HR in comparison to the reference measurement is small (3.4 BPM). For BP prediction AlexNet network with training of all layers with finger patch PPG data shows better performance and produces BP with an average MAE of 8.1 mmHg and 6.05 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. Without any need to turn on any LED, the finger patch can save power and will have the potential for long-term health monitoring without frequently charging batteries.

In recent studies, electroencephalogram (EEG) signals are acquired intraorally from the palate region. However, intraoral EEG study is a less explored research area and its challenges are yet to be investigated. In this study, we look into the possibility of studying EEG signals from various intraoral locations and investigate the sources of motion artifacts during intraoral EEG measurements. Later, we propose a sensor fusion of EEG electrodes and accelerometer module to monitor intraoral EEG signal and intraoral motions simultaneously. The EEG electrodes, accelerometer, and sensor read-out circuitry are integrated with a mandibular advancement device (MAD). The system is battery-operated and uses a Bluetooth 5.0 transceiver to send data wirelessly. The smart MAD is used to acquire intraoral EEG and accelerometer data and a MATLAB-based algorithm is implemented using empirical mode decomposition (EMD) and independent component analysis (ICA) to decompose the EEG signal components. The decomposed ICA components containing intraoral motion artifacts are then mapped with the motion events extracted from the accelerometer data to identify the motion-corrupted data segments. The ICA components containing intraoral motions are then denoised by nullifying the motion-corrupted data segments. A motion artifact reduced intraoral EEG is reconstructed from the denoised ICA components. The efficacy of the sensor fusion and the proposed algorithm are demonstrated by quantifying the signal-to-noise ratio (SNR) difference and percentage artifacts reduction based on correlation analysis from the EEG signals before and after motion artifacts reduction. Later, the processed intraoral EEG signals are also analyzed for the detection of ‘eye open’ and ‘eye close’ activities in the presence of intraoral motions. The device along with the algorithm will have potential applications for motion artifact-free intraoral EEG monitoring.