Extending the battery lifetime of wearable sensors with embedded machine learning

@article{Fafoutis2018ExtendingTB,
  title={Extending the battery lifetime of wearable sensors with embedded machine learning},
  author={Xenofon Fafoutis and Letizia Marchegiani and Atis Elsts and James Pope and Robert J. Piechocki and Ian Craddock},
  journal={2018 IEEE 4th World Forum on Internet of Things (WF-IoT)},
  year={2018},
  pages={269-274}
}
Smart health home systems and assisted living architectures rely on severely energy-constrained sensing devices, such as wearable sensors, for the generation of data and their reliable wireless communication to a central location. However, the need for recharging the battery regularly constitutes a maintenance burden that hinders the long-term cost-effectiveness of these systems, especially for health-oriented applications that target people in need, such as the elderly or the chronically ill… 

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