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

Giovanni Schiboni, Celia Martin Vicario, Juan Carlos Suarez, Federico Cruciani, Oliver Amft

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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.
Original languageEnglish
Pages (from-to)1-14
Number of pages15
JournalIEEE Transactions on Mobile Computing
Issue number12
Publication statusPublished (in print/issue) - 1 Dec 2022

Bibliographical note

Publisher Copyright:


  • Biomedical monitoring
  • Context modeling
  • Information rates
  • Receivers
  • Sensors
  • Wearable computers
  • Wearable sensors


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