AI-Driven Biotelemetry: Enhancing IoT-Based Human Area Networking with RedTacton for Smart Healthcare

Biotelemetry based on the Internet of Things (IoT) and artificial intelligence (AI) is revolutionizing predictive health monitoring by making it possible to collect, analyze and diagnose physiological data in real time. With the use of ESP32 microcontrollers for effective data processing and RedTacton technology for secure data transmission, this study presents an AI-powered predictive health monitoring system. The system uses integrated biological sensors to continually monitor vital signs, such as blood glucose, heart rate, SpO₂, ECG, body temperature and stress levels. For remote analysis and visualization, sensor data is securely transmitted to an IoT cloud platform using RedTacton-based human body connection. Based on comparison, RedTacton outperforms Bluetooth, NFC and RF-based networks in terms of power efficiency, security and dependability. With its inexpensive cost and integrated wireless connectivity, the ESP32 microcontroller also shows to be a better option than gadgets like the Raspberry Pi Pico W and STM32F4. Support Vector Machines (SVM) for stress analysis, Convolutional Neural Networks (CNN) for ECG classification, Random Forest for glucose prediction and Long Short-Term Memory (LSTM) networks for anomaly detection are some of the machine learning models that are integrated into the AI framework. Prompt alarm generating and real-time anomaly identification are guaranteed via a Python-based solution. By demonstrating lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), experimental validation versus the ESP8266 demonstrates the efficacy of the ESP32. The system incorporates advanced security features like multi-factor authentication (MFA), AES-256 encryption and tamper-proof enclosures to mitigate cybersecurity threats.