## 3 Materials and Methods
### Design and Architecture of NeuroPredict Platform
In addition, these microcontrollers facilitate version-controlled, transparent, and replicable software updates, which are critical for future MDR and AI Act compliance. From a privacy-by-design perspective, local edge computing using Raspberry-Pi-class platforms helps to preprocess and pseudonymize data before transfer to the cloud, addressing key GDPR requirements. Collectively, these technical, operational, and regulatory-aligned characteristics have proven relevant in the development of other solutions by the NeuroPredict platform team, confirming their suitability for the current experimental validation phase. Initial preprocessing is performed on edge-level devices, which additionally segment activity windows, align timestamps, and normalize across channels. Filtering and segmentation are part of this preprocessing. Depending on the configuration, certain motion characteristics, including acceleration magnitude, are calculated locally or during backend processing. Data are sent from edge nodes to the backend via USB or secured Wi-Fi, depending on the setup. Device type, session ID, sensor approach, firmware version, and other metadata headers are included in every transfer that is sent across encrypted HTTPS channels. Each transmission's metadata, including the sensor ID and session timestamp, is recorded to assist with data origin traceability and synchronized consistency [5]. The structured data streams are received, indexed, and processed by the _Analytics and Inference Layer_ on the backend. The platform relies on a time-series storage approach based on comma-separated values (CSVs), with metadata and session-level folder patterns kept in corresponding JavaScript Object Notation (JSON) files. This layout makes it easier to integrate with dashboard displays and analytical services and allows subsequent components to parse data consistently. Fast querying and longitudinal comparisons between time points and users are rendered possible by the session-level index in which each datasetis registered. The backend analytics layer incorporates machine learning algorithms to facilitate trend analysis and real-time classification. These methods, which are written in Python and trained on labeled datasets created through simulated user situations, include decision trees (DTs), random forests (RFs), support vector machines (SVMs), and extreme gradient boosting (XGBoost) classifiers. Preprocessed characteristics taken from cognitive interaction modules and multimodal sensor streams are processed by these models. Version control is incorporated into the ML pipeline's design to ensure consistency for training data, model parameters, and inference outputs. The _Application Layer_ is associated with the management of the process logic that connects the analytics infrastructure and the user-facing interface, handling alert process flow, and orchestrating user interactions. This layer, which is located in a cloud environment (e.g., ICIPRO), manages user authentication, session control, access rights, and the context-sensitive distribution of alerts and AI-driven insights. It acts as the platform's functional core, converting machine learning results into structured messages and then making them ready for visualization and processing. This layer controls the spread of metadata, thus guaranteeing that every single output stream has model versions, inference confidence ratings, and data authenticity properly attached. Future deployment scenarios, including asynchronous notifications, user-specific dashboards, and interaction with clinical decision support systems or electronic health records, are supported by the Application Layer's modular, scalable design. The NeuroPredict platform's visualization front end, the _Dashboard Layer_, is designed for a variety of user types, including patients, physicians, and informal caregivers.