Version 1
: Received: 14 November 2017 / Approved: 14 November 2017 / Online: 14 November 2017 (05:39:18 CET)
Version 2
: Received: 29 January 2018 / Approved: 30 January 2018 / Online: 30 January 2018 (09:03:40 CET)
Version 3
: Received: 3 February 2018 / Approved: 6 February 2018 / Online: 6 February 2018 (05:37:13 CET)
How to cite:
Sucerquia, A.; López, J.D.; Vargas-Bonilla, F. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer. Preprints2017, 2017110087. https://doi.org/10.20944/preprints201711.0087.v3
Sucerquia, A.; López, J.D.; Vargas-Bonilla, F. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer. Preprints 2017, 2017110087. https://doi.org/10.20944/preprints201711.0087.v3
Sucerquia, A.; López, J.D.; Vargas-Bonilla, F. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer. Preprints2017, 2017110087. https://doi.org/10.20944/preprints201711.0087.v3
APA Style
Sucerquia, A., López, J.D., & Vargas-Bonilla, F. (2018). Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer. Preprints. https://doi.org/10.20944/preprints201711.0087.v3
Chicago/Turabian Style
Sucerquia, A., Jose David López and Francisco Vargas-Bonilla. 2018 "Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer" Preprints. https://doi.org/10.20944/preprints201711.0087.v3
Abstract
The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.
triaxial accelerometer; wearable devices; fall detection; mobile health-care; SisFall; Kalman filter
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
