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Article

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

1
Department of Electrical & Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK
2
Department of Electronic Systems, Aalborg University, Aalborg Ø 9220, Denmark
3
Institute of Electronics and Computer Science (EDI), Dzerbenes 14, Riga LV-1006, Latvia
4
Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Kgs. Lyngby 2800, Denmark
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1655; https://doi.org/10.3390/s20061655
Received: 21 February 2020 / Revised: 10 March 2020 / Accepted: 13 March 2020 / Published: 16 March 2020
(This article belongs to the Special Issue Smart Mobile and Sensor Systems)
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. View Full-Text
Keywords: wearable systems; embedded machine learning; embedded classifiers; intelligent duty-cycling; health IoT wearable systems; embedded machine learning; embedded classifiers; intelligent duty-cycling; health IoT
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MDPI and ACS Style

Zalewski, P.; Marchegiani, L.; Elsts, A.; Piechocki, R.; Craddock, I.; Fafoutis, X. From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems. Sensors 2020, 20, 1655. https://doi.org/10.3390/s20061655

AMA Style

Zalewski P, Marchegiani L, Elsts A, Piechocki R, Craddock I, Fafoutis X. From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems. Sensors. 2020; 20(6):1655. https://doi.org/10.3390/s20061655

Chicago/Turabian Style

Zalewski, Pawel, Letizia Marchegiani, Atis Elsts, Robert Piechocki, Ian Craddock, and Xenofon Fafoutis. 2020. "From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems" Sensors 20, no. 6: 1655. https://doi.org/10.3390/s20061655

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