## 3 Materials and Methods
### Blackbox Devices' Concept and Development
The system uses an adaptive data packet framework, resulting in only pertinent and priority information for transmission, edge-level data preprocessing, and this lowers the total amount of raw signals, and dynamic protocol selection depending on the operational context to accomplish this. In particular, Wi-Fi transmission facilitates the transfer of higher-resolution biomedical data to cloud platforms for additional analysis, whilst BLE allows for short-range, low-power communication among sensors and local processing units. This hybrid communication approach ensures low-latency data sharing, which is important for real-time health monitoring, while optimizing energy efficiency and network bandwidth consumption. The system constantly determines between BLE and Wi-Fi in order to improve data transmission by considering the trade-offs between data throughput, communication range, and power efficiency. Although BLE guarantees minimal power consumption for short-range exchanges, its data speeds are constrained, and it performs most effectively for sending low-volume, sensitive information. The reliable transmission of high-resolution biomedical data is made possible by Wi-Fi's greater bandwidth and range, but at the expense of higher power consumption. The intrinsic interference mitigation approaches included into BLE and Wi-Fi protocols are used to lessen any signal interference in high-density environments. To further boost transmission accuracy under overloaded network contexts, future system versions might consider adaptive communication approaches. The following sections present the key modules and protocols utilized within the system, emphasizing their role in enabling seamless and secure data exchange. _Data Flow Structure Within the Monit4 Healthy System_  
Various sensors track vital parameters such as heart rate, SpO2, temperature, and EMG signals. These sensors communicate through standardized wireless protocols, ensuring seamless data transmission. The edge level of the Monit4 Healthy system is where integrated feature extraction and multi-sensor data synchronization are carried out. Individual preprocessing stages (such as noise filtering, timestamp alignment, and feature extraction) are applied to physiological signals from ECG, EMG, PPG, and GSR sensors. The features extracted are subsequently categorized into integrated data packets for transmission and real-time analysis. At the microcontroller level, each signal is subjected to distinct filtering and amplification based on its frequency and amplitude features. This is complemented by analog-to-digital conversion and timestamp-based alignment. After being synchronized, the signals are sent to an edge computing unit for initial signal processing. Despite the current lack of a formal fusion approach (such as rule-based fusion), the system architecture enhances context awareness by supporting simultaneous data processing and displaying correlated physiological parameters in real time. Future stages of development will involve the integration of advanced sensor fusion algorithms to provide complex health metrics based on multi-sensor inputs. The data flowchart in Figure 5 illustrates the process of multi-sensor data acquisition, synchronization, preprocessing, and transmission in the Monit4 Healthy system. It represents the flow of data from initial acquisition through multi-sensor systems to final data transmission, ensuring that the physiological signals from the patient are accurately monitored, processed, and transmitted to healthcare professionals for further analysis and timely interventions. A detailed description of the data flow and processing workflow is presented as follows:  
* _Data Layer--Multi-Sensor Data Acquisition:_ The process starts with the Data Layer, where physiological signals are continuously collected by various sensors integrated within the Monit4 Healthy system. The key sensors involved in data acquisition include the Medical Blackbox, Gaitband, Blackbox Act, and EMG Blackbox.