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

• Published in: Energies (Basel) Vol. 11; no. 11; p. 2866
• Main Authors: Gharghan, Sadik Kamel, Mohammed, Saleem Latteef, Al-Naji, Ali, Abu-AlShaeer, Mahmood Jawad, Jawad, Haider Mahmood, Jawad, Aqeel Mahmood, Chahl, Javaan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2018
• Subjects: 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
• DOI: 10.3390/en11112866

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.