English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Fall detection by built-in tri-accelerometer of smartphone

MPS-Authors
There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

He, Y., Li, Y., & Bao, S.-D. (2012). Fall detection by built-in tri-accelerometer of smartphone. In 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics (pp. 184-187). Piscataway, NJ, USA: IEEE.


Cite as: https://hdl.handle.net/21.11116/0000-0001-8F69-9
Abstract
In this study, a fall detection system based on the data acquired from a waist-mounted smartphone has been developed in a real-time environment. The built-in tri-accelerometer was utilized to collect the information about body movement. At the same time, the smartphone is able to classify the data for activity recognition. Body motion can be classified into five different patterns, i.e. vertical activity, lying, sitting or static standing, horizontal activity and fall. If a fall is suspected, an automatic Multimedia Messaging Service (MMS) will be sent to pre-selected people, with information including the time, GPS coordinate, and Google map of suspected fall location. The major advantage of the proposed system is the use of smartphone which is readily available to most people.