Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to det...

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Vydáno v:Energies (Basel) Ročník 11; číslo 11; s. 2866
Hlavní autoři: Gharghan, Sadik Kamel, Mohammed, Saleem Latteef, Al-Naji, Ali, Abu-AlShaeer, Mahmood Jawad, Jawad, Haider Mahmood, Jawad, Aqeel Mahmood, Chahl, Javaan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2018
Témata:
accelerometer sensor
Accelerometers
Accuracy
Blood pressure
Cameras
data-driven algorithm
Engineering
fall detection
Global positioning systems
GPS
Health care
International conferences
Localization
Methods
neural network
Neural networks
Older people
Sensors
tilt sensor
Wireless networks
wireless sensor network (WSN)
ZigBee
Baghdad Iraq
Australia
Iraq
ISSN:1996-1073, 1996-1073
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Shrnutí:Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en11112866