Detecting Falls Among Elderly Patients in Nursing Homes by Using Wireless Sensor Networks

Accidental falls among the senior population are the leading cause for seniors' admission to hospitals.Wireless Sensor Networks (WSN) can be used to efficiently detect falls of senior patients in nursing homes.While some fall detection methods focus on the acceleration of the patient, others register acceleration and body position to detect falls.In this paper, we describe a novel alert system using WSN capable of detecting falls based on the body position and bed occupancy.The system was created according to data collected in MoteView from seven test subjects.Accordingly, it was designed to sense three possible conditions: (1) patient being active; (2) patient lying in bed; or (3) if the patient has fallen down.The experimental portion of this research was performed at a nursing facility to further validate measurements previously collected in the laboratory.Furthermore, the system has been tested on three subjects for different types of falls and was found to detect all types of falls with high accuracy.In order to provide caregivers with constant alerts regarding patients' conditions a graphic user interface was created in LabView.Design of this system maximizes the capabilities of Memsic's Wireless Sensor Network Developmental Kit consisting of MICAz and MIB520 base station.Overall, the system provides a very simple and effective solution that yields high accuracy for detecting falls.

Authors

Institutions

Publication Details

Published
2025-01-22
DOI
https://doi.org/10.18260/1-2-1153-51104
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detecting Falls Among Elderly Patients in Nursing Homes by Using Wireless Sensor Networks

Sasan Haghani, Paul Cotae, Abayomi Dairo, Nikola Jovic et al.
Context-Aware Activity Recognition Systems
article

Detecting Falls Among Elderly Patients in Nursing Homes by Using Wireless Sensor Networks

Sasan Haghani, Paul Cotae, Abayomi Dairo, Nikola Jovic, ASHENAFI TESFAYE, AIME VALERE, YANNICK ROLAND KAMDEM
article en

Abstract

Accidental falls among the senior population are the leading cause for seniors' admission to hospitals.Wireless Sensor Networks (WSN) can be used to efficiently detect falls of senior patients in nursing homes.While some fall detection methods focus on the acceleration of the patient, others register acceleration and body position to detect falls.In this paper, we describe a novel alert system using WSN capable of detecting falls based on the body position and bed occupancy.The system was created according to data collected in MoteView from seven test subjects.Accordingly, it was designed to sense three possible conditions: (1) patient being active; (2) patient lying in bed; or (3) if the patient has fallen down.The experimental portion of this research was performed at a nursing facility to further validate measurements previously collected in the laboratory.Furthermore, the system has been tested on three subjects for different types of falls and was found to detect all types of falls with high accuracy.In order to provide caregivers with constant alerts regarding patients' conditions a graphic user interface was created in LabView.Design of this system maximizes the capabilities of Memsic's Wireless Sensor Network Developmental Kit consisting of MICAz and MIB520 base station.Overall, the system provides a very simple and effective solution that yields high accuracy for detecting falls.

University of the District of Columbia (US)
U.S. Department of Defense, Office of Naval Research
Openalex Percentile: Top 99%
Context-Aware Activity Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Detecting Falls Among Elderly Patients in Nursing Homes by Using Wireless Sensor Networks — Sasan Haghani, Paul Cotae, et al. · (2025) | TGRS Research Map | TGRS