System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks.

Area of Science:

• Gerontology
• Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• Global population aging increases demand for healthcare services.
• Falls are a prevalent and serious health risk for older adults, causing significant injuries and mortality.
• Existing fall detection methods may have limitations in privacy or effectiveness for elderly individuals living alone.

Purpose of the Study:

• To design and evaluate a fall detection system for elderly individuals living alone.
• To utilize very low-resolution thermal sensor arrays for unobtrusive monitoring.
• To compare the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (Bi-LSTM) algorithms for fall detection.

Main Methods:

• Development of a fall detection system using low-resolution thermal sensor arrays.
• Implementation and comparison of LSTM, GRU, and Bi-LSTM deep learning algorithms.
• Validation of the system's accuracy in detecting falls.

Main Results:

• The Bi-LSTM algorithm demonstrated the highest accuracy at 93% in fall detection.
• The system effectively utilizes low-resolution thermal data for fall detection.
• Performance metrics indicate the system's potential for real-world application.

Conclusions:

• The developed thermal-based fall detection system shows significant promise for enhancing the safety of elderly individuals living alone.
• Bi-LSTM is an effective algorithm for this specific fall detection application.
• This technology can serve as a valuable tool for accident prevention and clinical support.