## 5 Discussion  
This paper does not aim to present the NeuroPredict platform as a fully validated prototype, but rather as an evolving platform currently under laboratory-based testing. The fall prevention module is highlighted as a demonstrative use case, with the remaining functionalities being under active development. It is important to note that the current results represent an experimental evaluation of modular technological components within the NeuroPredict platform, rather than a complete clinical platform deployment. As a next step, participatory co-design and clinical validation will be carried out to ensure usability and acceptance in real-world settings. This discussion reflects on the technical maturity of the NeuroPredict platform and the implications of its modular architecture, rather than evaluating real-world clinical performance or patient acceptability. Although user interaction is not part of this stage, internal validation using structured questionnaires and simulated sessions helped refine core functionalities. The platform is designed to incorporate a clear and transparent informed consent process, ensuring that, upon deployment, individuals will understand the nature and scope of data collection and AI-driven decisions. The NeuroPredict platform is distinct from existing digital health systems addressing MS, PD, or AD through its integrated sensing environment and unified inference pipeline. By combining custom-built devices (e.g., GaitBand, Blackbox modules) with commercial wearables, it enables a versatile and high-resolution data acquisition approach. Emphasis on data synchronization, edge preprocessing, and layered compliance strategies supports the system's adaptability to various deployment scenarios, including semi-clinical and remote monitoring contexts. As a comprehensive digital health system for managing and monitoring neurodegenerative diseases, the outcomes presented in this paper illustrate the NeuroPredict platform's technological potential and early functional consistency. Specifically, repeated end-to-end session operation, consistent sensor synchronization, and reliable data transfer across the preprocessing, inference, and data collection components demonstrate this consistency. The platform's modular architecture allows for real-time collection, preprocessing, and inference throughout a range of ambient and physiological signals while preserving traceability and flexibility in compliance with new regulations pertaining to AI and medical device standards. The base data collection patterns, edge-to-cloud communication protocols, and AI-based classification pipelines have been determined to be functional and in accordance with the platform's stated clinical use cases by its layered architecture and controlled laboratory testing. With testing performed solely in controlled laboratory settings, making use of simulated user scenarios, this platform is presently in the preclinical testing stage. The functionalities are still not confirmed on actual patient populations, even though these experiments have shown consistent performance throughout the platform's layered architecture, including real-time data gathering, edge processing, and machine learning inference. Data alignment, label reliability, traceability visualization, and results consistency monitoring via the secure dashboard interface were all part of the testing procedure. AI's role in the NeuroPredict platform is directly aligned with current trends in healthcare, where digital tools are increasingly being used for more precise, real-time monitoring and predictive analytics. In the NeuroPredict platform, AI modules are configured to extract clinically relevant features such as gait variability, cardiovascular fluctuations, and sleep irregularities from multimodal sensor streams, supporting early trend detection and event classification. These continuous assessments enable healthcare providers to detect early signs of NDD progression, improving the likelihood of timely interventions.