In this paper, we present VYRE, a ring-based oscillometric wearable designed for low-burden and robust frequent blood pressure monitoring. VYRE revisits the clinically established oscillometric method — widely accepted in arm and wrist form factors because of its high accuracy — and extends it to a compact ring form factor, currently realized as a proof-of-concept prototype. The key innovation is the ability to derive oscillometric signals on the finger by inducing controlled inflation and deflation to capture arterial oscillations in response to circumferential tension. This enables accurate estimation of blood flow dynamics within the digital arteries for blood pressure inference. VYRE leverages a lightweight model to estimate systolic and diastolic pressures from the measured oscillations observed from a vibration sensor integrated into the ring. Compared to PPG-based methods, this approach offers significant improvement in robustness to signal drift, ambient light variations, and skin tone differences. Each measurement requires a user-initiated ∼ 40-second quiet hold, after which the system returns systolic and diastolic readings without per-user calibration. In an IRB-approved study involving 71 participants, VYRE achieves mean absolute errors of 6.36 mmHg for systolic and 4.96 mmHg for diastolic pressure relative to a reference cuff, with biases of − 1.27mmHg and 0.0mmHg, respectively. The standard deviations of 7.87 mmHg (SBP) and 6.22 mmHg (DBP) meet the AAMI requirements (≤ 8mmHg), and 95% limits of agreement fall within [ − 16.69, 14.15] mmHg for systolic and [ − 12.2, 12.19] mmHg for diastolic pressure. Correlation with the reference is strong (Pearson r = 0.75 for SBP, r = 0.67 for DBP), confirming consistent tracking. The system maintained accuracy across finger sizes and hand postures. User feedback indicated that 84% of participants rated the device as comfortable or very comfortable, and 70% expressed willingness to use it daily. These results confirm VYRE’s practicality, accuracy, and potential as a compact, on-demand blood pressure monitoring solution.

EEG signals contain highly sensitive information about an individual's mental state, cognitive processes, and health conditions, making privacy preservation crucial. With the rise of commercial headwear capable of capturing EEG signals, developing robust mechanisms for ensuring privacy of such data is imperative. This work aims to protect EEG data privacy in cloud-based processing systems by sending intermediate output after neural network layer splitting to the cloud. We propose a novel holistic Combined Privacy Metric (CPM) that quantifies privacy leakage between raw EEG signals and intermediate outputs. Our study focuses on EEG-based seizure detection using a 1D CNN architecture, achieving accuracy of 96.25%. We evaluate various splitting configurations to optimize the trade-off between privacy preservation and computational efficiency. We find that splitting after the second convolutional layer achieves a CPM of 0.82 with a modest client-side model size of 509kB. This approach significantly enhances EEG data privacy while enabling effective cloud-based analysis, potentially facilitating wider adoption of secure EEG technologies in healthcare and research applications.

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, head-based signal processing systems often produce complex signals, making wearable inference for diagnosis impractical, especially when the signals are weak. This is common with head-worn sensors due to poor contact. Moreover, the real-time transmission of a large corpus of physiological signals over extended periods consumes significant power and time, limiting the viability of battery-dependent physiological monitoring headwear. To address these issues, this paper presents a deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:585 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the clinic and deploy the solution on a commonly used embedded AI chip for headwear systems (i.e., ARM Cortex). The proposed framework achieves a 91% seizure detection accuracy, confirming the approach's reliability, practicality, and scalability.