AI-based geriatric care management system for achieving smart health | |
| Author | Jayanth, Dharavath |
| Call Number | AIT RSPR no.RS-25-01 |
| Subject(s) | Artificial intelligence--Medical applications Medical care |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | As global populations age at an accelerating rate, healthcare systems face increasing pressure to deliver accessible, efficient, and timely care to elderly individuals. Traditional caregiving models, often reliant on a younger workforce, are becoming increasingly unsustainable due to demographic shifts and resource limitations, resulting in delayed interventions, reduced in home support, and slower emergency responses. This research presents the Intelligent Geriatric Care Management System a real-time AI-powered platform that integrates simulated IoMT sensor data with advanced analytics to support proactive, continuous health management. The system continuously collects vital signs including heart rate, blood pressure, and SpO₂ from sensor-based streams, processes them for anomaly detection, and applies predictive assessment to identify early indicators of potential health deterioration. Its architecture combines a conversational AI assistant, powered by OpenAI’s GPT and Anthropic’s Claude, for personalized health interpretation; a real-time alert mechanism that issues voice prompts and escalates to emergency email notifications when abnormal readings are detected; and a monthly reporting module that generates AI-enhanced summaries and visual trend charts for long-term health tracking. By uniting continuous sensor-based monitoring with adaptive AI-driven interaction, I-GCMS delivers a scalable, automated, and personalized solution that bridges the gap between routine health tracking and timely intervention. This integration not only enhances safety and awareness for elderly users but also demonstrates how emerging AI and IoMT technologies can work together to strengthen preventive healthcare models in real-world community and home settings. |
| Year | 2025 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing and Geographic Information Systems (RS) |
| Chairperson(s) | Sarawut Ninsawat;Tripathi, Nitin Kumar (Co-chairperson) |
| Examination Committee(s) | Virdis, Salvatore G.P.;Sanit Arunplod |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Research Studies Project Report (M.Eng.) - Asian Institute of Technology, 2025 |