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

Aqeel Jawad,Saleem Mohammed,Haider Jawad,Sadik Gharghan,Ali Al-Naji,Mahmood Abu-AlShaeer,Javaan Chahl

doi:10.3390/en11112866

Abstract

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.

Highlights

The worldwide population of elderly who are more than 65 years old is expected to grow to 1 billion in 2030, and the percentage of individuals aged 20–64 years will become 35% of the population [1]
The first scenario was conducted in the LOS environment, (i) four bytes for identification of the beacon nodes and angle of fall, (ii) two bytes for RSSI1 value and the other scenario was applied in the NLOS environment
When the tilt sensor in the fall detection system (FDS) detects an elderly fall, the Zigbee is awakened and starts to measure the data of the angle of incidence, the shock that occurs when the subject falls to the ground, and the received signal strength indicator (RSSI) values of the three beacon nodes

Summary

Introduction

The worldwide population of elderly who are more than 65 years old is expected to grow to 1 billion in 2030, and the percentage of individuals aged 20–64 years will become 35% of the population [1]. In vital sign observation applications, patients and elderly people wear sensors that manage their vital parameters. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services

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Conclusion