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A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults.

Deepika Mohan¹, Peter Han Joo Chong¹, Jairo Gutierrez²

¹Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)

|July 12, 2025

Summary

This study introduces a cooperative artificial intelligence (AI) model for predicting elderly falls using vital signs and daily activities. The AI system achieved high accuracy, enhancing safety and preventive care for older adults.

Keywords:

ADLs deep belief networks fall risk prediction fuzzy logic meta-model random forest vital signs

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Area of Science:

Gerontology
Artificial Intelligence
Health Informatics

Background:

Older adults are significant users of healthcare services and are vulnerable to falls.
There is a need for cost-effective e-health technologies to support independent living for seniors.
Artificial intelligence (AI) and machine learning (ML) show potential for fall prediction and health monitoring.

Purpose of the Study:

To introduce a novel cooperative AI model for forecasting fall risk in the elderly.
To combine predictions from two AI models for enhanced accuracy.
To improve preventive care and well-being for older adults.

Main Methods:

Developed a cooperative AI model integrating two distinct AI predictors.
AI1 model utilized Fuzzy Logic based on vital signs.
AI2 model employed a Deep Belief Network (DBN) analyzing Activities of Daily Living (ADLs).
A meta-model combined outputs for a comprehensive fall risk prediction.

Main Results:

The cooperative AI model demonstrated 85.71% sensitivity.
Achieved 100% specificity in fall risk prediction.
Reported 90.00% overall prediction accuracy compared to the Morse Falls Scale (MFS).

Conclusions:

Deep learning-based cooperative systems can significantly improve the well-being of older adults living alone.
The proposed model offers a more precise method for fall risk assessment.
Enhanced fall risk assessment facilitates improved preventive care strategies for the elderly.