Verva: Sleep Science-Informed Algorithms for Extreme Runtime Wearables

Continuous health monitoring through wearables is constrained by limited battery life and inefficient data transmission. Verva addresses this challenge by applying sleep science-informed intelligence to significantly extend wearable runtime without compromising data quality.

At the core of Verva is a set of adaptive algorithms that optimize how physiological data is processed and transmitted. Collected signals are summarized into sleep parameters relevant to fatigue assessment-a form of domain-specific semantic data compression-reducing unnecessary data transfer.

Data transmission frequency dynamically adapts based on the individual’s sleep stage, while a lazy data-send strategy improves the payload-to-overhead ratio by transmitting only when clinically meaningful.

On the data collection side, energy efficiency is further improved through adaptive sampling of predictable time-series signals and dynamic sensing strategies that activate sensors only during periods of interest, allowing components to remain in low-power sleep states when not needed.

Together, these techniques enable ultra-low-power wearable systems that support long-term health monitoring, fatigue assessment, and sleep analytics -unlocking new possibilities for scalable, real-world healthcare applications.