## 3 Materials and Methods
### Monit4 Healthy System Architecture and Design
Figure 1: Overview of the Monit4 Healthy system architecture, showing the interaction between data collection (wearable and portable medical devices), wireless communication (BLE and Wi-Fi modules), edge processing (Raspberry Pi), cloud storage and analytics (ICIPRO platform), and visualization layers (clinician dashboards and mobile interfaces). The diagram highlights the structured flow of biomedical data—acquired via multi-sensor fusion—transmitted securely from the Medical Blackbox and other devices to the cloud infrastructure for real-time monitoring, anomaly detection, and visualization within connected health ecosystems. To guarantee organized aggregation as well as secure handling, real-time health data gathered from wearable devices are sequentially processed through many levels in the Monit4 Healthy system. In addition to transmitting raw sensor data, the shift from edge computing to cloud infrastructure additionally involves preprocessed signals that have already been filtered and structured for additional analysis. The integrity and accuracy of physiological and biomechanical parameters are maintained while bandwidth consumption is reduced using this sequential method. The structural organization and interaction between these layers are visually represented in Figure 1, which illustrates the Monit4 Healthy system architecture. This figure provides a comprehensive overview of how data flow from the initial sensor measurements to real-time decision-making and visualization tools. It highlights the integration of hardware and software components, showcasing the seamless connectivity between IoT devices, cloud infrastructure, and end-user applications. This architecture was designed for ensuring the system's ability to deliver accurate, efficient, and scalable health monitoring solutions. The Monit4 Healthy system integrates a suite of specialized biomedical IoT-based devices, each designed for specific health monitoring applications:  
* _Medical Blackbox_: A multifunctional enclosure that integrates various medical-grade sensors, designed to facilitate efficient data acquisition and processing. It captures and analyzes vital parameters such as heart rate, SpO\({}_{2}\), carbon dioxide levels, and GSR. This device integrates high-precision sensors for real-time physiological assessment, ensuring the accurate detection of health anomalies. * _Gaitband_: A wearable device designed for real-time gait pattern monitoring, optimized for ergonomic comfort and sensor placement stability. Equipped with an LSM303 DLHC magnetometer and accelerometer, the Gaitband continuously monitors gait patterns, posture stability, and movement dynamics. The SAM-M8Q GPS (Global Positioning System) module enhances location tracking and movement analysis, while the HM-10 Bluetooth module and ESP8266 (NodeMCU) facilitate secure wireless data transmission. By analyzing motion data, the device detects irregular walking patterns, potential fall risks, and neuromuscular impairments, providing valuable insights for mobility assessment and rehabilitation. * _EMG Blackbox_: A specialized enclosure engineered for housing the EMG sensor array, ensuring both signal integrity and protection from external interference. Utilizes surface electromyography (sEMG) sensors for neuromuscular function assessment, measuring electrical muscle activity to monitor conditions such as muscle fatigue and neurodegenerative disorders. * _AcU Blackbox_: A device that employs advanced optical spectrometry techniques for non-invasive uric acid monitoring in urine samples. The device provides real-time biochemical analysis, enabling the early detection of metabolic imbalances. * _Patient personal data_: Based on personal medical history and previous measured parameters and information from the Monit4 Healthy database, clinicians can issue personalized medical recommendations to the patients under their care, in order to communicate clinical or lifestyle actions that the patient should follow.