## 3 Materials and Methods  
### Monit4 Healthy System Architecture and Design  
The Monit4 Healthy system was designed using a modular sensor fusion architecture that ensures scalability, interoperability, and real-time data acquisition. The system is structured across multiple levels. The architecture consists of five core layers:  
* _Data Layer_: This layer involves the real-time collection of physiological and environmental data using advanced biomedical sensors integrated into multiple monitoring devices. * _Communication Layer_: This layer facilitates efficient and secure data transmission. Wearable devices rely on BLE for short-range communication, while high-bandwidth data transfers occur via Wi-Fi to ensure seamless connectivity with cloud-based storage and processing systems. * _Edge Processing Layer_: This layer is responsible for local data processing and initial analysis before transmitting physiological and biomechanical data to the cloud. This layer operates on a Raspberry Pi, which processes raw sensor signals from the Medical Blackbox, AcU Blackbox, EMG Blackbox, and Gaitband IoT-based devices (designed and developed for the Monit4 Healthy system). Compared to a cloud-only setup, this approach improves latency, minimizes bandwidth usage by preprocessing data locally, and ensures real-time responsiveness. The implementation of edge computing in IoT systems introduces several significant advantages: (1) reduced latency, by processing data directly at the edge nodes, ensuring fast response times essential for real-time healthcare monitoring; (2) bandwidth efficiency, as only relevant, processed data are transmitted to cloud servers, reducing network congestion; (3) system autonomy and fault tolerance, allowing continued local operation even when cloud services are temporarily unavailable; and (4) enhanced privacy and security, as sensitive patient data are filtered and processed locally, limiting external exposure and improving compliance with data protection standards. Figure 1 highlights the data flow between edge and cloud layers [56]. * _Processing and Cloud Layer and Application Sublayer_: The Processing and Cloud Layer is responsible for data storage, structured representation, and alerting mechanisms. This layer aggregates sensor data transmitted from the Edge Processing Layer, organizing them for structured analysis and long-term storage. It includes interactive data processing tools, enabling customizable health metric representations and structured reports. A cloud-based alert and notification system operates within this layer, continuously analyzing incoming physiological and biomechanical data. It detects deviations from predefined health thresholds and triggers automated alerts through multi-channel notifications. Secure storage mechanisms are integrated to ensure data encryption, controlled access, and regulatory compliance for sensitive health information. The Application Sublayer acts as the central processing interface for managing sensor data, health alerts, and system configurations. This layer facilitates remote monitoring functionalities, allowing healthcare providers to manage real-time and historical patient data. It is responsible for user authentication, system configuration, and data synchronization across cloud servers and edge devices. It also handlescommunication between medical professionals and patients, ensuring the seamless integration of monitoring data with personalized medical recommendations. * _Visualization Layer:_ This layer focuses on presenting real-time health insights in a user-friendly format. It provides interactive interfaces for different stakeholders, including patients, clinicians, and researchers, ensuring efficient access to health data. Features include dynamic dashboards, trend graphs, and anomaly detection alerts, ensuring that medical professionals and patients receive timely information.