Paper: Beat Nyquist Sampling?

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Extremely low-power sensing systems need to minimise their sensor sampling and data processing. Can we do better than state-of-the-art in sub-Nyquist sampling and compressive sensing? Yes, we can!

Our new paper Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems presents a context-aware compressive sensing approach that replaces the uniform random sampling assumption with a probability distribution depending on estimated signal context. Our approach can be implemented in low-power wearable and implantable sensor devices that use a remote system (e.g. a smartphone) to decompress the data.

Abstract

This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13% to 22% of energy, while achieving similar pattern recognition performance and reconstruction error.

Reference

Giovanni Schiboni, Celia Martin Vicario, Juan Carlos Suarez, Federico Cruciani, Oliver Amft, "Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems", IEEE Transactions on Mobile Computing, In Press.

The full text is available via publication link above or IEEExplore: https://ieeexplore.ieee.org/document/9424443

Contact

Giovanni Schiboni

Prof. Dr. Oliver Amft

Friedrich-Alexander-Universität Erlangen-Nürnberg