Fall detection using smartwatch sensor data with accessor architecture

Anne Ngu, Yeahuay Wu, Habil Zare, Andrew Polican, Brock Yarbrough, Lina Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

This paper proposes using a commodity-based smartwatch paired with a smartphone for developing a fall detection IoT application which is non-invasive and privacy preserving. The majority of current fall detection applications require specially designed hardware and software which make them expensive and inaccessible to the general public. We demonstrated that by collecting accelerometer data from a smartwatch and processing those data in a paired smartphone, it is possible to reliability detect (93.8% accuracy) whether a person has encountered a fall in real-time. By wearing a smartwatch as a piece of jewelry, the well-being of a person can be monitored in real-time at anytime and anywhere as contrasted to being confined in a particular facility installed with special sensors and cameras. Using simulated fall data acquired from volunteers, we trained a fall detection model off-line that can be composed with a data collection accessor to continuously analyze accelerometer data gathered from a smartwatch to detect minor or serious fall at anytime and anywhere. The accessor-based architecture allows easy composition of the fall-detection IoT application tailored to heterogeneity of devices and variation of user’s need.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2017, Proceedings
EditorsElena Karahanna, Indranil Bardhan, Hsinchun Chen, Daniel Dajun Zeng
PublisherSpringer Verlag
Pages81-93
Number of pages13
ISBN (Print)9783319679631
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventInternational Conference on Smart Health, ICSH 2017 - Hong Kong, Hong Kong
Duration: Jun 26 2017Jun 27 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10347 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Smart Health, ICSH 2017
CountryHong Kong
CityHong Kong
Period6/26/176/27/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ngu, A., Wu, Y., Zare, H., Polican, A., Yarbrough, B., & Yao, L. (2017). Fall detection using smartwatch sensor data with accessor architecture. In E. Karahanna, I. Bardhan, H. Chen, & D. D. Zeng (Eds.), Smart Health - International Conference, ICSH 2017, Proceedings (pp. 81-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10347 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67964-8_8