Snoring and breath-stopping are key symptoms of sleep apnea. Most existing studies primarily focus on wearable devices or smartphone-based systems. Wearable devices can be uncomfortable, while smartphone-based systems often require specific angles, distances, or positions, making them sensitive to environmental changes. This paper proposes a contactless and engagement-free system for snoring and breath-stopping detection using a seismic sensor. Distinguishing between snoring, breath-stopping, and normal breathing from raw data alone is challenging. Snoring features typically reside in a higher frequency range than breath-stopping and normal breathing, with breath-stopping features appearing in a lower frequency range. Calculating the differential and integral of raw data can enhance features in low and high frequencies, respectively. We introduce AFFCL, an attention feature fusion and contrastive learning framework to leverage information from differential and integral signals. AFFCL generates both shared and exclusive features from the differential and integral signals and employs an attention mechanism for feature fusion. Additionally, cluster-level supervised contrastive learning in AFFCL further enhances system performance. Our system has been performed 5-fold cross-validation on 44 people, which achieves an average accuracy of 93.40% and an F1 score of 92.42%. The accuracy for detecting breath-stopping, snoring, and normal breathing are 89.54%, 94.60%, and 96.06%, respectively. Evaluation results demonstrate that our system effectively identifies breath-stopping and snoring.

In this study, we introduce BedDot, the first contact-free and bed-mounted continuous blood pressure monitoring sensor. Equipped with a seismic sensor, BedDot eliminates the need for external wearable devices and physical contact, while avoiding privacy or radiation concerns associated with other technologies such as cameras or radars. Using advanced preprocessing techniques and innovative AI algorithms, we extract time-series features from the collected bedseismogram signals and accurately estimate blood pressure with remarkable stability and robustness. Our user-friendly prototype has been tested with over 75 participants, demonstrating exceptional performance that meets all three major industry standards, which are Association for the Advancement of Medical Instrumentation (AAMI), Food and Drug Administration (FDA) and the British and Irish Hypertension Society (BHS), and outperforms current state-of-the-art deep learning models for time series analysis. As a non-invasive solution for monitoring blood pressure during sleep and assessing cardiovascular health, BedDot holds immense potential for revolutionizing the field.

Health monitoring is essential for both humans and animals in daily life. While numerous health monitoring systems have been developed, the majority are designed exclusively for either humans or animals, and most require direct physical contact. We have developed BedDot, a contactless health monitoring system for both humans and animals using a seismic sensor. BedDot can be deployed in various environments, such as bed and seat settings for humans, as well as in cages for animals, to monitor occupancy, heart rate (HR), respiratory rate (RR), and blood pressure (BP). Our system demonstrates high accuracy in the clinical experiments of 150 patients and 16 dogs and cats.

This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.

We conducted an online experimental survey to evaluate attitudes toward an authorization for contact (AFC) program allowing researchers to contact patients about studies based on electronic record review. A total of 1070 participants were randomly assigned to 1 of 3 flyers varying in design and framing. Participants were asked to select concerns about and reasons for signing up for AFC. Logistic regression and latent class analysis were conducted. The most commonly selected concerns included needing more information (43%), privacy (40%), and needing more time to think (28%). A minority were not interested in participating in research (16%) and did not want to be bothered (15%). Latent class analysis identified clusters with specific concerns about privacy, lack of interest in research, and not wanting to be bothered. A novel flyer with simple and positive framing was associated with lower odds of both not wanting to be bothered (P = .01) and not being interested in research (P = .01). Many concerns about AFC programs appear nonspecific. Addressing privacy, lack of interest in research, and not wanting to be bothered warrant further study as ways to enhance recruitment.