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An Automatic Remote Health Risk Assessment system based on LSTM for elderly
Summary
This study introduces an AI system using LSTM deep learning to predict elderly health risks remotely. The system accurately identifies health risks, offering a new strategy for home-based elder care monitoring.
Area of Science:
- Gerontology
- Artificial Intelligence
- Biomedical Engineering
Background:
- The aging population presents significant healthcare challenges, necessitating innovative monitoring solutions.
- Remote health assessment systems are crucial for timely intervention in elderly care.
Purpose of the Study:
- To design and validate a Long Short-Term Memory (LSTM) network-based system for automatic remote health risk assessment in the elderly.
- To enable timely responses from care teams by predicting vital signs and calculating health risk levels.
Main Methods:
- Developed a system with wireless physiological sensing, vital sign prediction using five LSTM neural networks, and a simplified National Early Warning Score (NEWS) for risk calculation.
- Predicted key vital signs: systolic blood pressure (SBP), pulse rate (PR), respiratory rate (RR), temperature (TEMP), and oxygen saturation (SPO2).
Main Results:
- The system achieved a 74% accuracy in risk identification.
- Mean Absolute Errors (MAEs) for predicted vital signs were within acceptable ranges, demonstrating prediction reliability.
Conclusions:
- An automated remote health risk assessment system leveraging deep learning, specifically LSTM, is a viable strategy for elderly home-based monitoring.
- This technology can enhance proactive care and improve health outcomes for the elderly population.
