Sweat is a readily accessible bodily fluid for detecting biomarkers such as pH, glucose etc., enabling continuous and non-invasive assessment of the well-being of individuals. Our proposed work aims at leveraging pulse oximeter chips in current-day fitness trackers for real-time continuous monitoring of pH in sweat. We achieve that by fabricating a highly responsive and long-term reusable pH sweat sensor on a flexible material to achieve skin conformity, targeting the sensor to work at the reflected infrared (880nm) and red (660nm) photoplethysmograph (PPG) signal intensities recorded by pulse oximeters. The sensor can be readily mounted atop any wearable with a pulse oximeter. We have successfully demonstrated a low-cost, low-power, highly-responsive and longterm reusable wrist-worn wearable prototype, pH Watch, for realtime continuous monitoring of pH value of sweat. We conducted on-body trials with 10 participants and pH Watch achieves an accuracy of ≈91%. We also showed that the integration of our sweat sensor does not hinder the pulse oximeter from measuring heart rate and SpO 2 , and users can continue with their daily activities with motion artifacts removed efficiently from PPG signals using the TROIKA framework, resulting in heart rate and SpO 2 measurements with an accuracy of ≈95% and ≈96% respectively when validated against commercial finger pulse oximeter measurements. To the best of our knowledge, pH Watch is the first demonstration of a reusable sweat sensor that can be readily integrated into today's smart watches with pulse oximeters, paving the way for ubiquitous sensing of biomarkers. * Both authors contributed equally to this research.• Human-centered computing → Ubiquitous and mobile devices; • Applied computing → Health care information systems.
Personal data collected from today's wearable sensors contain a rich amount of information that can reveal a user's identity. Differential privacy (DP) is a well-known technique for protecting the privacy of the sensor data being sent to community sensing applications while preserving its statistical properties. However, differential privacy algorithms are computationally expensive, requiring user-level random noise generation which incurs high overheads on wearables with constrained hardware resources. In this paper, we propose SeRaNDiP -- which utilizes the inherent random noise existing in wearable sensors for distributed differential privacy. We show how various hardware configuration parameters available in wearable sensors can enable different amounts of inherent sensor noise and ensure distributed differential privacy guarantee for various community sensing applications with varying sizes of populations. Our evaluations of SeRaNDiP on five wearable sensors that are widely used in today's commercial wearables -- MPU-9250 accelerometer, ADXL345 accelerometer, BMP 388 barometer, MLP 3115A2 barometer, and MLX90632 body temperature sensor show a 1.4X-1.8X computation/communication speedup and 1.2X-1.5X energy savings against state-of-the-art DP implementation. To the best of our knowledge, SeRaNDiP is the first framework to leverage the inherent random sensor noise for differential privacy preservation in community sensing without any hardware modification.
Existing artificial skin interfaces lack on-skin AI compute that can provide fast neural network inference for time-critical applications. In this paper, we propose AI-on-skin - a wearable artificial skin interface integrated with a neural network hardware accelerator that can be reconfigured to run diverse neural network models and applications. AI-on-skin is designed to scale to the entire body, comprising tiny, low-power, accelerators distributed across the body. We built 7 AI-on-Skin application prototypes and our user trials show AI-On-Skin achieving 20X and 50X speedup over off-body inference via Bluetooth and on-body centralized microprocessor based inference approach respectively. We also project the power performance of AI-on-skin with our accelerator fabricated as silicon chips instead of emulated on FPGAs and show 10X further power savings. To the best of our knowledge, AI-on-Skin is the first ever wearable prototype to demonstrate skin interfaces with on-body AI inference.
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