A Discreet Wearable Long-Range Emergency System Based on Embedded Machine Learning

Charalampos Orfanidis, Rayen Bel Haj Hassen, Armando Kwiek, Xenofon Fafoutis, Martin Jacobsson

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Abstract

Low-Power Wide Area Networks have contributed in several parts of the Internet of Things ecosystem during the last years by enabling long range, robust and low power communication. Machine Learning for embedded systems has also assisted the advancement of the Internet of Things by identifying patterns and increasing the accuracy of predicting events and behaviours. At the same time, wearable and mobile systems are less obtrusive, consuming less energy and have more computing resources. In this paper we combine these three components and propose a low cost wearable system based on a regular shoe and off-the-shelf electronics which is able to recognize foot gestures and transmit messages over long range, in cases of emergency. The evaluation considers an application scenario where the user performs specific foot gestures to trigger the transmission of an emergency message, during other activities (e.g., walking). The proposed wearable system would benefit a user who is in danger and attempts to notify her/his emergency contacts in a discreet manner. Results show that the proposed system is able to identify the intended foot gestures with 98% accuracy.
Original languageEnglish
Title of host publicationProceedings of the 5th IEEE PerCom Workshop on Pervasive Health Technologies
Number of pages6
PublisherIEEE
Publication date2021
ISBN (Print)978-1-7281-4716-1
Publication statusPublished - 2021
Event6th IEEE PerCom Workshop on Pervasive Health Technologies - Virtual event, Kassel, Germany
Duration: 22 Mar 202026 Mar 2020

Workshop

Workshop6th IEEE PerCom Workshop on Pervasive Health Technologies
LocationVirtual event
CountryGermany
CityKassel
Period22/03/202026/03/2020

Keywords

  • IoT
  • LPWAN
  • Pervasive Health
  • Wearable
  • Foot Gesture

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