## 3 Materials and Methods
### Blackbox Devices' Concept and Development
* _Communication Layer--Data Transmission:_ Once the data are collected by the sensors, they are transmitted through two communication channels: \(\odot\) BLE, used for short-range communication, ideal for wearable or portable sensors that require low-power consumption. \(\odot\) Wi-Fi, used for higher-bandwidth, long-range data transmission, enabling the system to send large datasets such as high-resolution ECG and other complex signals to the cloud for further processing. * _Edge Processing Layer--Initial Signal Processing:_ After the data are transmitted, they enter the Edge Processing Layer. Here, the raw signals undergo initial processing, including the following: \(\odot\) Signal Conditioning: The filtering and amplification of the data to remove noise and enhance signal quality. \(\odot\) Feature Extraction: Relevant features such as heart rate from ECG and PPG signals, muscle activity from EMG, and skin conductance levels from GSR are extracted for further analysis. \(\odot\) Analog-to-Digital Conversion: The analog signals from the sensors are converted to digital format to facilitate further computational processing. A Raspberry Pi or similar edge computing devices are used in this layer to perform the initial processing, reducing the amount of raw data sent to the cloud and ensuring that only essential information is transmitted, thus minimizing bandwidth usage and latency. * _Data Structuring and Integration:_ Once the feature extraction is performed, the processed data are structured and integrated for synchronization across multiple sensors. This step ensures that the data from all sensors are aligned in time by applying timestamps, facilitating multi-sensor data fusion and providing a more comprehensive evaluation of physiological conditions. The integration of data from various sensors such as ECG, EMG, and GSR enhances the system's ability to detect abnormalities and generate alerts when necessary. * _Data Transmission to the Cloud:_ After data fusion, the structured data are transmitted using the communication protocols (BLE or Wi-Fi) to the cloud server (ICIPRO). This enables the following: \(\odot\) Data Storage: The cloud serves as the repository for the processed data, ensuring they are stored securely and accessible for further analysis. \(\odot\) Data Synchronization: The real-time synchronization of the patient's health data across devices and systems allows healthcare professionals to access the most up-to-date information at any time. * _Application Sublayer--Real-Time Monitoring and Alerts:_ At this stage, the structured and synchronized data are accessible via an Application Sublayer, where healthcare professionals and patients can view the data in real time through interactive dashboards. These dashboards provide visualizations such as the following: \(\odot\) Trend graphs showing the patient's health metrics over time. Alerts generated when the data deviate from predefined thresholds, indicating potential health risks. This allows for timely interventions, as healthcare providers can receive immediate alerts if a patient's vital signs indicate a need for attention. * _Visualization Layer--Health Data Visualization:_ Finally, the processed data are displayed in the Visualization Layer, providing clear and easy-to-interpret health metrics for both patients and medical professionals. This layer facilitates the following:
* The real-time visualization of key health parameters such as heart rate, SpO\({}_{2}\), gait patterns, and muscle activity. * Alert notifications that inform healthcare providers about critical changes in the patient's condition. * Access to multiple functionalities that provide real-time, updated data and information related to the patient's profile, based on the information stored in the system's database. These functionalities are considered and utilized as part of a personalized, proactive, and preventive health management support system for the patient.