Year: 2025
Type: article
Abstract: Elderly healthcare has long relied on manual monitoring and hospital visits, often failing to provide timely responses to critical health conditions. With the increasing geriatric population and rise in age-related illnesses, the lack of continuous health surveillance has become a major contributor to preventable emergencies. Traditional systems lack predictive intelligence and real-time alerting, resulting in delayed interventions and higher fatality risks. To tackle this challenge, we introduce an AI-driven emergency notification system that provides continuous surveillance of key health indicators like heart rate, blood pressure, and body temperature in senior citizens. The system, developed using the Flask framework, incorporates machine learning algorithms to analyze historical and live health data, triggering immediate alerts when anomalies or risk patterns are detected. Results from simulation and prototype testing indicate improved detection accuracy, timely risk prediction, and reduced response latency. This approach provides a cost-effective, scalable solution for proactive elderly care in both urban and rural settings
Authors: Darshana Dandina, Dr. Shivprasad K M
Institution: Visvesvaraya Technological University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414442433
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