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Collaborative Edge-Cloud Computing for Personalized Fall Detection

Abstract : The use of smartwatches as devices for tracking one’s health and well-being is becoming a common practice. This paper demonstrates the feasibility of running a real-time personalized deep learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch.
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Conference papers
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https://hal.inria.fr/hal-03287665
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Submitted on : Thursday, July 15, 2021 - 6:10:01 PM
Last modification on : Thursday, July 15, 2021 - 6:31:52 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:03:42 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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Vangelis Metsis, Anne H. Ngu, Shaun Coyne, Priyanka Srinivas. Collaborative Edge-Cloud Computing for Personalized Fall Detection. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.323-336, ⟨10.1007/978-3-030-79150-6_26⟩. ⟨hal-03287665⟩

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