Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Schiboni G, Vicario CM, Suarez JC, Cruciani F, Amft O (2021)

Publication Type: Journal article

Publication year: 2021

Journal

IEEE Transactions on Mobile Computing Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TMC.2021.3077731

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.

Authors with CRIS profile

Giovanni Schiboni Lehrstuhl für Digital Health Oliver Amft Lehrstuhl für Digital Health

Involved external institutions

University of Ulster

United Kingdom (GB)

How to cite

APA:

Schiboni, G., Vicario, C.M., Suarez, J.C., Cruciani, F., & Amft, O. (2021). Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems. IEEE Transactions on Mobile Computing. https://dx.doi.org/10.1109/TMC.2021.3077731

MLA:

Schiboni, Giovanni, et al. "Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems." IEEE Transactions on Mobile Computing (2021).

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