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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

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear.

Rocío Aznar-Gimeno¹, Gorka Labata-Lezaun¹, Ana Adell-Lamora¹

¹Department of BigData and Cognitive Systems, Instituto Tecnológico de Aragón, ITAINNOVA, María de Luna 7-8, 50018 Zaragoza, Spain.

Entropy (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces smart footwear to monitor elderly individuals, detecting falls and mobility issues for enhanced safety. The system uses advanced sensors and artificial intelligence to provide real-time alerts, supporting independent living.

Keywords:

artificial neural networks assistive technology deep learning elderly people smart footwear wearable devices

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Area of Science:

Gerontology
Assistive Technology
Biomedical Engineering

Background:

The aging European population presents challenges in elder care and maintaining independence.
Existing assistive technologies often lack the integration and real-time monitoring needed for proactive health management.

Purpose of the Study:

To develop a smart footwear system for real-time detection of elderly behaviors and potential health risks.
To enhance the safety and independence of elderly individuals in urban environments.

Main Methods:

Utilized a smart footwear system with 20 sensors (piezoelectric, accelerometer, temperature).
Employed a hierarchical structure of cascading binary models with artificial neural networks (ANN) and deep learning.
Implemented convolutional layered ANN and multilayer perceptrons for event detection.

Main Results:

Accurately detected various user behaviors including sitting, standing, walking, running, and tripping.
Achieved an average accuracy of 0.84 and an area under the ROC curve of 0.96 for event detection.
Enabled near-real-time risk warnings for relatives.

Conclusions:

The proposed smart footwear system effectively monitors elderly individuals' mobility and detects potential health risks.
This technology supports independent living for the elderly by providing timely alerts and enhancing safety.
The system demonstrates the potential of integrated sensor technology and AI in geriatric care.