Wearable technology is advancing health monitoring by enabling real-time, privacy-preserving physiological analysis. However, traditional devices often rely on cloud processing, restricting access to raw sensor data and limiting the progress of health-related research. To overcome these limitations, we introduce OmniBuds, a programmable earable research platform that enables multimodal bio-sensing and on-device learning while providing direct access to raw physiological data, fostering advancements in health perception and wearable intelligence. It integrates PPG, temperature, IMUs, and multiple microphones, leveraging an embedded ML accelerator for efficient real-time processing. This paper presents its design, architecture, and applications, demonstrating its potential to shape the future of health-aware wearables.

Neuromorphic computing is promising to achieve unprecedented energy efficiency by emulating the human brain’s mechanism. Conventional neuromorphic accelerators employ split-and-merge method to map spiking neural networks’ inputs to surpass the fan-in capabilities of a single neuron core. However, this approach gives rise to the risk of accuracy compromise and extra core usage for the merging process. Moreover, it requires excessive data movement and clock cycles to aggregate spikes generated by partial sums instead of total sums obtained from different cores with substantial power and energy overhead. This work presents a novel approach to addressing the challenges imposed by the split-and-merge method. We propose an energy-efficient, reconfigurable neuromorphic processor that leverages several key techniques to mitigate the above issues. First, we introduce a partial sum router circuitry that enables in-network computing (INC), eliminating the need for extra merge cores. Second, we adopt software-defined Networks-on-Chip (NoCs) by leveraging predefined, efficient routing, eliminating power-hungry routing computation. At last, we incorporate fine-grained power gating and clock gating techniques for further power reduction. Experimental results from our test chip demonstrate the lossless mapping of the algorithm and exceptional energy efficiency, achieving an energy consumption of 1.63 pJ/SOP at 0.48 V. This energy efficiency represents a 22.4% improvement compared to the state-of-the-art results. Our proposed neuromorphic processor provides an efficient and flexible solution for neural network processing, mitigating the limitations of the traditional split-and-merge approach while delivering superior energy efficiency.

Conventional neuromorphic accelerators primarily leverage split-merge method to accommodate a neural network that is beyond a single core's size, leading to possible accuracy loss, extra core usage and significant power and energy overhead. This work presents an energy-efficient, reconfigurable neuro-morphic processor to address the problem by (i) a partial sum router circuitry that enables in-network computing to remove the need of extra merge cores; (ii) software-defined Networks-on-Chip that eliminates the power-hungry routing compute and (iii) fine-grained power gating and clock gating technique for power reduction. Our test chip achieves lossless mapping as the algorithm and an energy efficiency of 1.7pJ/SOP at 0.5V, 19% lower than state-of-the-art result.

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.

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 long-term reusable wrist-worn wearable prototype, pH Watch, for real-time continuous monitoring of pH value of sweat. We conducted on-body trials with 10 participants and pH Watch achieves an accuracy of $\approx$91%. We also showed that the integration of our sweat sensor does not hinder the pulse oximeter from measuring heart rate and SpO\textsubscript2, 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\textsubscript2 measurements with an accuracy of $\approx$95% and $\approx$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.

Most present day fitness trackers and smart watches measure critical health indicators such as heart rate, SpO2 concentration, sleep cycle etc. but they fail in their ability to track health indicators at the molecular level. This has attracted rapid research in the development of chemical sensors which can non-invasively measure analytes available in raw biofluids such as sweat, tears and urine. Of all the available raw bio-fluids, sweat can be obtained non-intrusively and readily, and thus is the most suitable choice for continuous real-time monitoring of indicators at a molecular level.