Fall detection using smartwatch sensor data with accessor architecture

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

Resultado de la investigación: Conference contribution

23 Citas (Scopus)


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.

Idioma originalEnglish (US)
Título de la publicación alojadaSmart Health - International Conference, ICSH 2017, Proceedings
EditoresElena Karahanna, Indranil Bardhan, Hsinchun Chen, Daniel Dajun Zeng
EditorialSpringer Verlag
Número de páginas13
ISBN (versión impresa)9783319679631
EstadoPublished - 2017
Publicado de forma externa
EventoInternational Conference on Smart Health, ICSH 2017 - Hong Kong, Hong Kong
Duración: jun 26 2017jun 27 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10347 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


ConferenceInternational Conference on Smart Health, ICSH 2017
País/TerritorioHong Kong
CiudadHong Kong

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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