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All Journal International Journal of Electrical and Computer Engineering
Sathish Kumar Selvaperumal
Asia Pacific University of Technology and Innovation (APU)
Computer Science & IT Electrical & Electronics Engineering

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Accelerometer-based elderly fall detection system using edge artificial intelligence architecture Osama Zaid Salah; Sathish Kumar Selvaperumal; Raed Abdulla
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4430-4438

Abstract

Falls have long been one of the most serious threats to elderly people's health. Detecting falls in real-time can reduce the time the elderly remains on the floor after a fall, hence avoiding fall-related medical conditions. Recently, the fall detection problem has been extensively researched. However, the fall detection systems that use a traditional internet of things (IoT) architecture have some limitations such as latency, high power consumption, and poor performance in areas with unstable internet. This paper intends to show the efficacy of detecting falls in a resource-constrained microcontroller at the edge of the network using a wearable accelerometer. Since the hardware resources of microcontrollers are limited, a lightweight fall detection deep learning model was developed to be deployed on a microcontroller with only a few kilobytes of memory. The microcontroller was installed in a low-power wide-area network based on long range (LoRa) communication technology. Through comparative testing of different lightweight neural networks and traditional machine learning algorithms, the convolutional neural network (CNN) has been shown to be the most suited, with 95.55% accuracy. The CNN model reached inference times lower than 37.84 ms with 61.084 kilobytes storage requirements, which implies the capability to detect fall event in real-time in low-power microcontrollers.
Co-Authors Osama Zaid Salah Raed Abdulla