From bits of data to bits of knowledge—an on-board classification framework for wearable sensing systems

Pawel Zalewski, Letizia Marchegiani, Atis Elsts*, Robert Piechocki, Ian Craddock, Xenofon Fafoutis

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Original languageEnglish
Article number1655
JournalSensors (Switzerland)
Issue number6
Number of pages18
Publication statusPublished - 2 Mar 2020


  • Embedded classifiers
  • Embedded machine learning
  • Health IoT
  • Intelligent duty-cycling
  • Wearable systems

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