Signal Processing - March 2016 - 71
SIGNAL PROCESSING FOR ASSISTED LIVING
Moeness G. Amin, Yimin D. Zhang,
Fauzia Ahmad, and K.C. (Dominic) Ho
Radar Signal Processing for Elderly Fall Detection
The future for in-home monitoring
R
IMAGE LICENSED BY INGRAM PUBLISHING,
WOMAN-© ISTOCKPHOTO.COM/SILVIAJANSEN
adar is considered an important technology for health monitoring and fall detection
in elderly assisted living due to a number of attributes not shared by other sensing
modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human's radar signal returns
differ in their Doppler characteristics, depending on the nature of the
human gross motor activities. These signals are nonstationary in
nature, inviting time-frequency analysis in both its linear
and bilinear aspects, to play a fundamental role in
motion identification, including fall features determination and classification. This article employs
real fall data to demonstrate the success of
existing detection algorithms as well as to
report on some of the challenges facing technology developments for fall detection.
Digital Object Identifier 10.1109/MSP.2015.2502784
Date of publication: 7 March 2016
1053-5888/16©2016IEEE
Introduction
The elderly population aged 65 years or
older is growing, and their ratio to the
population aged 20-64 will reach 35% in
2030 [1]. The worldwide population over
65 is projected to increase to one billion in
2030. An overwhelming majority of elderlies exercise self-care within their own
homes most of the time. One out of three
elderly people over the age of 65 will fall every
year, and the percentage rises for the elderly living in long-term care institutions. The fall can
result in injuries and reduced quality of life and,
unfortunately, it represents one of the leading causes of
death in the elderly population. Eventually, the elderly who
are at high risk of falling will have to move to institutionalized
care, which can cost approximately US$3,500 per month. Most seniors
are unable to get up by themselves after a fall, and it was reported that, even without direct injuries, half of those who experienced an extended period of lying on the floor
(more than an hour) died within six months after the incident. Therefore, prompt fall
detection saves lives, leads to timely interventions and most effective treatments, and
reduces medical expenses. Furthermore, it avoids placing major burdens on the elderly's
family. Driven by a pressing need to detect and attend to a fall, elderly fall detection has
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Table of Contents for the Digital Edition of Signal Processing - March 2016
Signal Processing - March 2016 - Cover1
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Signal Processing - March 2016 - Cover3
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