Preprint
Article

Kalman Filter Based Elderly Fall Detection with a Triaxial Accelerometer

This version is not peer-reviewed.

Submitted:

14 November 2017

Posted:

14 November 2017

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Abstract
The consequences of a fall on an elderly person can be diminished 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 objective population, or are not feasible to be implemented in real-life conditions. In this work we propose a Kalman-filter-based fall detection methodology that includes a periodicity detector to reduce the false positive rate. Moreover, 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 tested our approach with the SisFall dataset. Then, we validated it with a new round of simulated activities with young adults and an elderly person achieving 99.4 % of accuracy. Finally, we gave the devices to three elderly persons during two days for full-day validations. They continued with their normal life and the devices behaved as expected.
Keywords: 
triaxial accelerometer; wearable devices; fall detection; mobile health-care; SisFall; Kalman filter
Subject: 
Public Health and Healthcare  -   Public Health and Health Services
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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