System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks

Carla Taramasco; Yoslandy Lazo; Tomás Rodenas; Paola Fuentes; Felipe Martínez; Jacques Demongeot

doi:10.1007/s10916-019-1484-1

System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks

Carla Taramasco, Yoslandy Lazo, Tomás Rodenas, Paola Fuentes, Felipe Martínez, Jacques Demongeot

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-third of patients each year and often result in serious injuries that can lead to death. This paper describes the design of a fall detection system for elderly households living alone using very low resolution thermal sensor arrays. The algorithms implemented were LSTM, GRU, and Bi-LSTM; the last one mentioned being that which obtained the best results at 93% in accuracy. The results obtained aim to be a valuable tool for accident prevention for those patients that use it and for clinicians who manage the data.

Original language	English
Article number	50
Journal	Journal of Medical Systems
Volume	44
Issue number	2
DOIs	https://doi.org/10.1007/s10916-019-1484-1
Publication status	Published - 1 Feb 2020

Keywords

Bi-LSTM
Elderly surveillance
Emergency monitoring
Fall detection
GRU
LSTM

ASJC Scopus subject areas

Medicine (miscellaneous)
Information Systems
Health Informatics
Health Information Management

Access to Document

10.1007/s10916-019-1484-1

Cite this

@article{8d515b941a514a9b8cd78410f96edd37,

title = "System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks",

abstract = "The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-third of patients each year and often result in serious injuries that can lead to death. This paper describes the design of a fall detection system for elderly households living alone using very low resolution thermal sensor arrays. The algorithms implemented were LSTM, GRU, and Bi-LSTM; the last one mentioned being that which obtained the best results at 93% in accuracy. The results obtained aim to be a valuable tool for accident prevention for those patients that use it and for clinicians who manage the data.",

keywords = "Bi-LSTM, Elderly surveillance, Emergency monitoring, Fall detection, GRU, LSTM",

author = "Carla Taramasco and Yoslandy Lazo and Tom{\'a}s Rodenas and Paola Fuentes and Felipe Mart{\'i}nez and Jacques Demongeot",

year = "2020",

month = feb,

day = "1",

doi = "10.1007/s10916-019-1484-1",

language = "English",

volume = "44",

journal = "Journal of Medical Systems",

issn = "0148-5598",

publisher = "Springer New York",

number = "2",

}

TY - JOUR

T1 - System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks

AU - Taramasco, Carla

AU - Lazo, Yoslandy

AU - Rodenas, Tomás

AU - Fuentes, Paola

AU - Martínez, Felipe

AU - Demongeot, Jacques

PY - 2020/2/1

Y1 - 2020/2/1

N2 - The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-third of patients each year and often result in serious injuries that can lead to death. This paper describes the design of a fall detection system for elderly households living alone using very low resolution thermal sensor arrays. The algorithms implemented were LSTM, GRU, and Bi-LSTM; the last one mentioned being that which obtained the best results at 93% in accuracy. The results obtained aim to be a valuable tool for accident prevention for those patients that use it and for clinicians who manage the data.

AB - The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-third of patients each year and often result in serious injuries that can lead to death. This paper describes the design of a fall detection system for elderly households living alone using very low resolution thermal sensor arrays. The algorithms implemented were LSTM, GRU, and Bi-LSTM; the last one mentioned being that which obtained the best results at 93% in accuracy. The results obtained aim to be a valuable tool for accident prevention for those patients that use it and for clinicians who manage the data.

KW - Bi-LSTM

KW - Elderly surveillance

KW - Emergency monitoring

KW - Fall detection

KW - GRU

KW - LSTM

UR - http://www.scopus.com/inward/record.url?scp=85077617956&partnerID=8YFLogxK

U2 - 10.1007/s10916-019-1484-1

DO - 10.1007/s10916-019-1484-1

M3 - Article

C2 - 31907688

AN - SCOPUS:85077617956

SN - 0148-5598

VL - 44

JO - Journal of Medical Systems

JF - Journal of Medical Systems

IS - 2

M1 - 50

ER -

System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this