Optimizing Space and Time Complexity for IoT Infused Health Monitoring System

This paper describes a unique IoT-infused health monitoring system based on the ESP32 microcontroller, which is meant to capture and transmit real-time health parameters such as SpO2, body temperature, pulse rate, and electrocardiogram. The system integrates MAX30100 for SPO2 and pulse oximetry, DS18B20 for temperature sensing, and AD8232 for ECG monitoring, ensuring continuous data collection. The ESP32 processes this data locally and securely uploads it to both Blynk cloud and Google Sheets platforms, enabling healthcare providers to remotely monitor patients' health statuses and trends. An OLED display incorporated into the system provides immediate visual feedback to patients, promoting self-awareness and adherence to monitoring protocols. Optimization efforts have reduced system complexity, resulting in increased efficiency in terms of space utilization and data processing speed. The space complexity was lowered to 2kB from 5kB previously, and the time complexity before space optimization was O(n2), whereas after space optimization the time complexity was O(n). Successfully accomplished was the optimization in terms of time and spatial complexity. This integrated approach represents a scalable solution for personalized healthcare, facilitating proactive patient management, continuous monitoring, and data-driven decision-making. By leveraging IoT technologies, this system contributes to advancing telemedicine capabilities and enhancing patient outcomes in diverse healthcare settings.