Smart IoMT-Based Anomaly Detection System for Real-Time Health Monitoring

Abstract

The invention provides an intelligent Internet of Medical Things (IoMT)-based system for real-time monitoring of patient vitals and anomaly detection. The system uses IoMT devices to collect vital parameters, an AI-based module to detect anomalies, and a personalization engine for user-specific thresholds. Alerts are generated for anomalies, enabling timely medical interventions. This system ensures accurate and adaptive health monitoring, enhancing proactive healthcare management.

Specification

Description:The proposed invention is a Smart IoMT-Based Anomaly Detection System for Real-Time Health Monitoring (100) by Monitoring Patient Vitals. The system integrates advanced IoT-enabled medical devices with Artificial Intelligence (AI) algorithms to address the challenges of real-time health monitoring and anomaly detection. In this proposed method, IoMT Devices (101) consisting of various advanced sensors continuously measure critical vital parameters such as heart rate, blood pressure, oxygen saturation (SpO2), respiratory rate, and body temperature. These devices ensure non-invasive and uninterrupted data collection for comprehensive health monitoring. Power management (102) is carried out using both rechargeable and fixed battery system. The core feature AI-Based anomaly detection module (103) uses machine learning models trained to detect abnormalities in real time. These models utilize historical and real-time data to adapt to individual health profiles and provide accurate predictions of potential health risks. Here, Clustering-based anomaly detection is used for anomaly detection. Any abnormality when identified , the Alert and Notification system (106) generates alerts and notifications categorized by severity (e.g., low, moderate, critical). These alerts are transmitted to users, caregivers, or healthcare professionals via mobile apps, SMS, or dashboards, ensuring timely action. Vital data and anomaly records are securely stored on a cloud or edge server for long-term analysis. This enables trend analysis, preventive healthcare measures, and better decision-making for healthcare providers which is saved in Data Storage and Analytics (104). , C , Claims:1. A Smart IoMT-Based Anomaly Detection System for Real-Time Health Monitoring (100) comprising of:
Multiple Sensor IoMT module (101);
Power generation module (102);
AI-Based Anomaly Detection module (103);
PC with Arduino module (104);
Monitor with GUI (105);
Alert and Notification system (106);
2. A Smart IoMT-Based Anomaly Detection System for Real-Time Health Monitoring (100) comprising of AI-Based Anomaly Detection module (103) wherein the AI-based anomaly detection using cluster based detection and random forest classification is carried out.

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