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IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety.

Fayez Alfayez¹, Surbhi Bhatia Khan²

¹Department of Computer Science and Information, College of Science, Majmaah University, Al-Majmaah, Saudi Arabia.

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|October 9, 2023

Summary

This study introduces an IoT and blockchain-based fall detection system for seniors. The system accurately identifies falls and alerts caregivers, enhancing elderly safety.

Keywords:

IoT SSA TLBO blockchain deep learning elderly people

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Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Gerontology
Computer Science
Public Health

Background:

The growing elderly population faces increased risks of falls, posing a significant public health challenge.
Seniors living alone may struggle to seek timely assistance after a fall.
Existing fall detection systems often lack robust security and efficient emergency response mechanisms.

Purpose of the Study:

To develop an integrated fall detection system for the elderly utilizing the Internet of Things (IoT) and blockchain technology.
To enhance the accuracy and security of fall detection and emergency notification processes.
To provide a reliable solution for elderly fall incidents, improving response times and care.

Main Methods:

Data collection from wearable sensors (accelerometers, gyroscopes) followed by pre-processing (missing/null value handling).
Feature extraction using statistical methods, autocorrelation, and Principal Component Analysis.
A novel hybrid optimization algorithm (HSSTL) for feature selection and an optimized Convolutional Neural Network (CNN) within a TriNet architecture (including LSTM and RNN) for fall detection.
Secure storage and dissemination of fall alerts via a blockchain network.

Main Results:

The proposed system achieved high accuracy in fall detection, with a maximum accuracy of 0.974015 at an 80% learning rate.
The hybrid HSSTL optimization model effectively selected optimal features, enhancing the performance of the TriNet fall detection model.
Blockchain integration ensured secure storage of fall data and reliable emergency alerts to designated contacts.

Conclusions:

The developed IoT and blockchain-based system offers a promising solution for accurate and secure elderly fall detection.
The system's ability to provide rapid and secure emergency notifications can significantly improve outcomes for seniors experiencing falls.
Further research and implementation can lead to widespread adoption, enhancing the safety and well-being of the elderly population.