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Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation. 

While the global healthcare market of wearable devices has been growing significantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality.This study proposes PROS, an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-off between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) develop tiny machine learning models to eliminate irrelevant biosignal patterns, (ii) efficiently perform compressive sampling of relevant biosignals with appropriate sparse wavelet domains, and (iii) optimize hardware and OS operations to push processing efficiency. PROS also provides an abstraction layer, so the application only needs to care about detected relevant biosignal patterns without knowing the optimizations underneath.We have implemented and evaluated PROS on two open biosignal datasets with 120 subjects and six biosignal patterns. The experimental results on unknown subjects of a practical use case such as epileptic seizure monitoring are very encouraging. PROS can reduce the streaming data rate by 24X while maintaining high fidelity signal. It boosts the power efficiency of the wearable device by more than 1200\% and enables the ability to react to critical events immediately on the device. The memory and runtime overheads of PROS are minimal, with a few KBs and 10s of milliseconds for each biosignal pattern, respectively. PROS is currently adopted in research projects in multiple universities and hospitals. 

Face touch is an unconscious human habit. Frequent touching of sensitive/mucosal facial zones (eyes, nose, and mouth) increases health risks by passing pathogens into the body and spreading diseases. Furthermore, accurate monitoring of face touch is critical for behavioral intervention. Existing monitoring systems only capture objects approaching the face, rather than detecting actual touches. As such, these systems are prone to false positives upon hand or object movement in proximity to one's face (e.g., picking up a phone). We present FaceSense, an ear-worn system capable of identifying actual touches and differentiating them between sensitive/mucosal areas from other facial areas. Following a multimodal approach, FaceSense integrates low-resolution thermal images and physiological signals. Thermal sensors sense the thermal infrared signal emitted by an approaching hand, while physiological sensors monitor impedance changes caused by skin deformation during a touch. Processed thermal and physiological signals are fed into a deep learning model (TouchNet) to detect touches and identify the facial zone of the touch. We fabricated prototypes using off-the-shelf hardware and conducted experiments with 14 participants while they perform various daily activities (e.g., drinking, talking). Results show a macro-F1-score of 83.4% for touch detection with leave-one-user-out cross-validation and a macro-F1-score of 90.1% for touch zone identification with a personalized model. 

Frequent blood pressure monitoring is the key to diagnosis and treatments of many severe diseases. However, the conventional ambulatory methods require patients to carry a blood pressure (BP) monitoring device for 24 h and conduct the measurement every 10--15 min. Despite their extensive usage, wearing the wrist/arm-based BP monitoring device for a long time has a significant impact on users' daily activities. To address the problem, we developed eBP to measure blood pressure (BP) from inside user's ear aiming to minimize the measurement's impact on users' normal activities although maximizing its comfort level. The key novelty of eBP includes (1) a light-based inflatable pulse sensor which goes inside the ear, (2) a digital air pump with a fine controller, and (3) BP estimation algorithms that eliminate the need of blocking the blood flow inside the ear. Through the comparative study of 35 subjects, eBP can achieve the average error of 1.8 mmHg for systolic (high-pressure value) and -3.1 mmHg for diastolic (low-pressure value) with the standard deviation error of 7.2 mmHg and 7.9 mmHg, respectively. These results satisfy the FDA's AAMI standard, which requires a mean error of less than 5 mmHg and a standard deviation of less than 8 mmHg. 

Diagnosing hypertension or hemodialysis requires patients to carry a blood pressure (BP) monitoring device for 24 hours. Th erefore, wearing the wrist/arm-based BP monitoring device, in this case, has a signifi cant impact on users' daily activities. To address the problem, we developed eBP, an ear-worn device that measures blood pressure from inside the ear. Th rough the evaluation of 35 subjects, eBP can achieve the average error of 1.8 mmHg for systolic BP and -3.1 mmHg for diastolic BP with the standard deviation error of 7.2 mmHg and 7.9 mmHg, respectively.