@article{M8AA64284,
title = "CNN-LSTM Based Lonely Death Prevention Home AIoT System",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.7.1143",
author = "Yong-Ho Kim, Sung-Hyun Oh, Jeong-Gon Kim",
keywords = "AI, Lonely Death, IoT, CNN-LSTM, Pattern Prediction",
abstract = "In According to recent statistics, 79.1% of solitary death cases occur among young and middle-aged adults, extending beyond the elderly population and affecting all age groups. Additionally, 78.8% of individuals at risk of solitary death are from single-person households. This highlights the growing need for technologies capable of continuously monitoring movements in single-person households and providing immediate response in emergency situations. This paper proposes a real-time monitoring system that tracks and predicts hourly movements in single-person households. To achieve this, we developed a solitary death prevention system utilizing motion data and AI (Artificial Intelligence) for time-series predictions. The system employs PIR sensors (Passive Infrared Sensor) strategically placed in multiple locations within the home to address blind spots and expand the detection range. By implementing a Home AIoT (Artificial Intelligence of Things) system using a CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) model, the practicality of the system is validated."
}