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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.

Original publication




Conference paper

Publication Date



661 - 675