## 3 Materials and Methods
### Development Environment and Tools
#### 3.3.2 Hardware Components Used  
The Monit4 Healthy system integrates a selection of high-performance microcontrollers and sensor technologies to enable real-time health monitoring and environmental data acquisition. These components ensure efficient data processing, seamless connectivity, and high reliability, which are critical for IoT-driven healthcare applications. _Microcontrollers_  
* _ESP32_--Chosen for its dual-core architecture, integrated Wi-Fi and Bluetooth capabilities, and ultra-low power consumption, the ESP32 is particularly suited for remote patient monitoring systems that require real-time data transmission over wireless networks. Its deep-sleep functionality minimizes energy usage, ensuring extended operation in battery-powered medical devices [68]. * _STM32_--These are a family of ARM Cortex-based microcontrollers offering high computational efficiency and low power consumption, making them ideal for embedded medical applications requiring real-time processing. An STM32 microcontroller compresses the digital ECG signals using a basic matrix-vector multiplication technique based on compressed sensing [69]. * _Raspberry Pi_--Serving as an edge computing node, Raspberry Pi offers high processing power and versatile I/O capabilities, enabling the real-time preprocessing of biomedical signals, anomaly detection, and encrypted data transmission to cloud servers. This reduces network latency and offloads computational tasks from the central cloud infrastructure [70]. * **ATmega2560 Microcontroller**--Applications requiring extensive firmware storage and multitasking can benefit from the ATmega2560, an 8-bit AVR microcontroller with 256 KB of flash memory, 8 KB of SRAM, and 4 KB of EEPROM. Four UARTs, an SPI, I2C, and sixteen 10-bit analog-to-digital converter channels are among the many peripherals it supports. The ATmega2560, which runs at a clock speed of 16 MHz, provides dependable performance for activities involving real-time data collection and control while consuming little power, which is crucial for embedded health monitoring applications [71]. * **Arduino Mega Board**--With 54 digital input/output pins, 16 analog inputs, and various serial communication interfaces, the Arduino Mega board, which is based on the ATmega2560 microcontroller, has extensive I/O capabilities that enable integration with multiple biomedical sensors at once. The Arduino Mega is used in the Medical Blackbox device of the Monit4 Healthy system to collect and interpret physiological signals in real time. Strong and scalable health monitoring systems are made possible by its dependable interrupt management, hardware timers, and interoperability with common sensor libraries, which provide effective and coordinated data collection from multi-sensor arrays [72]. Currently, the Medical Blackbox is powered via a regulated 5V DC input, compatible with external USB power adapters and battery packs, ensuring safe and portable operation during laboratory testing. * _Biomedical Sensors_--Modules such as MAX30100, for heart rate and SpO2 monitoring via PPG; AD8232, for real-time ECG signal acquisition; a custom EMG module, for electromyography signal capture, designed with adjustable gain, bandpass filtering, and analog signal conditioning tailored for muscle activity monitoring; a GSR sensor, for galvanic skin response measurement, used in stress and autonomic nervous system assessments; and a digital temperature sensor, for continuous body temperature monitoring for real-time ECG signal acquisition, enable continuous physiological monitoring, facilitating the early detection of health anomalies. _Hardware Integration and Connectivity_  
In addition to the microcontrollers outlined above, the Monit4 Healthy system incorporates a range of hardware components that support efficient sensor integration, signal acquisition, and wireless communication.