Development of a fall detection system based on neural network featuring IoT-Technology

Nik Anwar, Nik Syahrim and Ng, Yong Jie and Ng, Wei Yu and Law, Cheng Quan (2021) Development of a fall detection system based on neural network featuring IoT-Technology. International Journal of Human and Technology Interaction, 5 (1). pp. 37-46. ISSN 2590-3551

Text 2021 IJHATI FALL DETECTION PAPER.PDF Download (1MB)

Abstract

Accidental falls are considered a major cause of accidents that could lead to serious injuries, paralysis, psychological damage, and even deaths, especially for the elderly. Therefore in this project, a neural network-based fall detection system that could automatically detect a fall event is proposed. The system is enhanced with Internet-ofThings (IoT) features that could reduce the response time and efficiently improve the prognosis of fall victims. A 10 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) module is connected to an Intel Edison with Mini Breakout board and mounted on a wearable waist-worn device to continuously record body movements. A backpropagation neural network algorithm has been developed to accurately distinguish falls from different postural transitions during activities of daily living (ADL). A body temperature and heartpulse monitoring device were developed for this system to provide the medical personnel additional information on the body condition of the fall victim. Using the latest IoT-technology, the system can be connected to the internet and provides a continuous and real-time monitoring capability. Once a fall accident happens, the system will be automatically triggered. This will activate an Android App through the Wi-Fi network that will then send an emergency SMS with the actual location and body conditions of the victim to a recipient. A series of falls and ADL simulations were performed by a group of subjects to test and validate the performance of the system. The experiment results showed that the proposed system could obtain a sensitivity of 95.5%, specificity of 96.4%, and accuracy of 96.3%.

Item Type:	Article
Uncontrolled Keywords:	Fall detection, Neural network, Voice response, Vital-sign monitoring, Internetof-Things (IoT)
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	13 Apr 2022 10:09
Last Modified:	13 Apr 2022 10:09
URI:	http://eprints.utem.edu.my/id/eprint/25824
Statistic Details:	View Download Statistic

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