Cost-Efficient AI-based Wearable Device for Real-Time Monitoring and Analysis

As the prevalence of chronic diseases increases and the demand for real-time health monitoring grows, the need for accessible and advanced wearable technology becomes essential. Conventional healthcare monitoring systems often do not offer access to real-time data and suffer from a lack of personalized analysis. Thus, our main goal is to provide real-time access and analysis of a reliable and cost-effective IoT device. This work develops a wearable IoT health monitoring device equipped with an ESP32 microcontroller and specialized sensors, including the MAX30102 for measuring blood oxygen saturation (SpO2) and heart rate (BPM), as well as a pulse sensor for capturing ECG (Electrocardiogram) signals. The system is integrating an AI-driven chatbot interface that deals with the data retrieved from the device and shares its feedback with the user. The wearable device is realized using an ESP32 microcontroller, known for its versatility and connectivity features. Health data from the MAX30102 sensor and the pulse sensor are streamed in real time with the help of the Blynk application via Wi-Fi. The chatbot is capable of interpreting user queries and interactively showing health data insights and visualizations. The system successfully captures and transmits real-time BPM, SpO2, and ECG data to the Blynk app, where the patient and the doctor visualize and monitor it. Users interact with the AI chatbot to receive personalized health status updates and risk assessments. The integration of real-time data with AI analytics allows immediate and actionable health insights, enhancing health management and monitoring. This IoT-based health monitoring system represents a significant advancement in wearable health technology, offering real-time data-driven insights into patient health. Future developments will focus on improving AI capabilities for more sophisticated health predictions and expanding the range of monitored vital signs.