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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Jafar Razmara¹, Mohammad Hassan Zaboli¹, Hadi Hassankhani²

¹University of Tabriz, Iran.

Health Informatics Journal

|January 5, 2017

Summary

This study predicts elderly fall risk using a neural network analyzing physiological factors. Combining psychological and public health data achieved over 91% accuracy, identifying elders at risk for falls.

Keywords:

elderly fall risk prediction neural networks physiological profile

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Published on: February 8, 2019

Area of Science:

Gerontology
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Falls are a major cause of morbidity and mortality in older adults.
Accurate prediction of fall risk is crucial for preventative interventions in elder care.

Purpose of the Study:

To predict fall risk in elders using a physiological profile approach.
To evaluate the effectiveness of a multilayer neural network for fall risk assessment.

Main Methods:

Collected physiological profiles from 200 elders via questionnaire.
Utilized a multilayer neural network with back-propagation for prediction.
Applied principal component analysis to identify high-impact risk factors.

Main Results:

Achieved ≈90% accuracy predicting falls based on psychological factors.
Achieved ≈87.5% accuracy predicting falls based on public factors.
Combined psychological and public factors yielded ≈91% prediction accuracy.

Conclusions:

The proposed neural network model effectively predicts fall risk in elders.
Combining psychological and public health factors enhances prediction accuracy.
This method offers reliable measurements for healthcare and physical therapy settings to identify at-risk individuals.