Abstract
AI and IoT-based biotelemetry are revolutionizing predictive health monitoring by enabling real-time physiological data gathering, analysis, and anomaly diagnosis. This study presents an AI-powered predictive health monitoring system that uses RedTacton technology for secure data sharing and NORDIC NRF7002 (PAIRED WITH NRF5340) for efficient processing. The system uses biological sensors to continuously measure blood glucose, heart rate, SpO2, ECG, temperature, and stress levels. RedTacton-based human body communication securely sends sensor data to an IoT cloud for remote analysis and visualization. RedTacton outperforms Bluetooth, NFC, and RF-based networks in terms of reliability, security, and power efficiency, according to a comparison study. Compared to competitors like the Raspberry Pi Pico W and STM32F4, the NORDIC NRF7002 (PAIRED WITH NRF5340) is a superior choice because to its low cost and built-in wireless capabilities. Machine learning models, including SVM for stress analysis, CNN for ECG classification, Random Forest for glucose prediction, and LSTM for anomaly detection, are used in the artificial intelligence framework. A Python-based system guarantees real-time anomaly identification and triggers alarms for timely response. Experimental validation, which compares this study with that of ESP8266, confirms the usefulness of NORDIC NRF7002 (PAIRED WITH NRF5340) in biotelemetry, as this study had lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). To combat cyber threats, security improvements include tamper-proof enclosures, multi-factor authentication, and AES-256 encryption.
