## 3 Materials and Methods
### Design and Architecture of NeuroPredict Platform
It is implemented as a secure web-based interface that transforms Application Layer processing outcomes into comprehensible visual overviews, including trend indicators, classification labels, time-aligned graphs, and feedback on cognitive performance. A review of the sensor inputs, preprocessing pipeline, and AI inference environment is made possible by the appropriate metadata associated with each session. The dashboard facilitates interpretability and accessibility, which are critical for making decisions in healthcare settings subject to the AI Act and MDR. The design provides an adaptable framework for multimodal interaction and feedback, and although it is presently implemented in a controlled laboratory setting, it is anticipated to be scalable for use in clinical and home-based settings. The _Communication and Security Layer_ performs throughout every single component of the NeuroPredict architecture, allowing for dependable, secure data transfer and preserving system integrity, even though it is not a stand-alone functional layer. All data flows are protected using HTTPS encryption, and communication across layers--for example, from acquisition devices to edge processors and from edge to backend services--is handled via secured channels (such as Wi-Fi and USB). To facilitate traceability and source validation, metadata related to every transmission--such as session ID, device type, and firmware version--is recorded. This layer is intended to grow with deployment situations that need sporadic or distant connectivity and protects data exchange secrecy and integrity. Despite being visible to users, it is essential for guaranteeing reliable and compatible communication amongst the system's dispersed components. The architectural layering of the NeuroPredict platform is purposefully designed to address the unique challenges of neurodegenerative disease management. The _Sensing and Acquisition Layer_ enables continuous and multidimensional data collection, capturing subtle motor and cognitive changes critical for early detection. The _Edge Processing Layer_ supports near-real-time anomaly detection and trend recognition, reducing latency for timely interventions. The _Communication and Transport Layer_ ensures secure and traceable data transmission, preserving the integrity of longitudinal patterns. The _Cloud Integration and Data Lake_ support the aggregation and retrospective analysis of long-term trajectories, enabling clinicians to access high-resolution patient histories for personalized care planning. Finally, the _Dashboard Layer_ delivers interpretable insights and alerts to healthcare professionals and caregivers, facilitating proactive adjustments to therapy or support strategies. This integrated architecture directly supports the continuous, adaptive, and explainable monitoring required for managing complex neurodegenerative disease trajectories. All the records from the database of the NeuroPredict platform provide retrospective analysis and repeatability, which is consistent with the latest documentation standards under MDR. In accordance with medical device standards, this architecture ensures lifecycle documentation and traceable validation of experimental outcomes. The regulatory and governance approach, which enforces data protection and ethical design standards, runs across the whole system. GDPR-compliant features, including data minimization, explicit consent, and pseudonymizing personal identifiers, are all included in the platform. To map data flows, evaluate related risks, and direct mitigation actions, a conceptual Data Protection Impact Assessment (DPIA) has been carried out. Role-based restrictions that are in line with internal testing protocols limit access to datasets and system operations. To prepare for future compliance requirements under the EU AI Act and the MDR, the platform features inference session recording and model version tracking. Future clinical dashboard prototypes are expected to provide functional assistance for comprehensibility, such as including significance depiction.