## 1 Introduction  
Multiple sclerosis (MS), Parkinson's disease (PD), and Alzheimer's disease (AD) are examples of neurodegenerative diseases (NDDs) that pose an increasing threat to global health. These disorders, which are characterized by a progressive and frequently irreversible decline in neuronal function, cause significant impairments in daily activities, mobility, and cognition. As the population ages, their prevalence is increasing, which necessitates improved diagnostic and treatment approaches in healthcare systems as well as long-term support networks [1; 2]. Few effective disease-modifying therapies are available, despite progress in our understanding of the pathophysiology of NDDs. To improve patient outcomes, customize interventions, and slow disease progression, early diagnosis and ongoing monitoring are essential. However, subtle preclinical changes are often missed by traditional diagnostic methods, which are often episodic and rely on complex imaging techniques. Such delays reduce the opportunity for prompt therapeutic intervention in conditions like AD and PD, where neuropathological changes occur years before clinical symptoms appear. In recent years, artificial intelligence (AI) and wearable technologies have opened new possibilities for the continuous and personalized monitoring of neurodegenerative diseases. By shifting from episodic clinical evaluations to real-time, context-aware analysis, these tools aim to capture subtle changes in motor and cognitive function that would otherwise go undetected. However, many current solutions remain limited in terms of integration, ethical oversight, and regulatory compliance. These gaps are what the NeuroPredict platform seeks to address through its unified and modular design. _The aim_ of this study is to present and experimentally evaluate, in a laboratory setting, the modular architecture of the NeuroPredict platform, with a specific focus on its fall prevention module. _The objectives_ are: (i) to describe the technological components and their integration; (ii) to report the results of laboratory-based evaluations using simulated user scenarios; and (iii) to discuss the platform's preparedness for future co-design, clinical validation, and regulatory compliance. _Identified shortcomings and research gap_: Previous research in AI-enabled neurodegenerative disease monitoring has shown several shortcomings: (i) fragmented focus on individual domains, such as motor or cognitive assessment, without a unified approach; (ii) insufficient integration of ethical, legal, and regulatory safeguards from the design phase; (iii) lack of participatory or user-centered design methodologies; and (iv) limited readiness for scalable, clinically deployable architectures. These gaps highlight the need for a cohesive, modular platform that can integrate multimodal data acquisition, advanced machine learning pipelines, and compliance-by-design principles for trustworthy neuro-monitoring. _Research questions_: To address these gaps, this study investigates the following research questions:  
* _RQ1_: How can a modular, scalable architecture integrate multimodal sensors to support neurodegenerative disease monitoring in a laboratory-based setting? * _RQ2_: What is the technical performance of fall detection algorithms implemented on the NeuroPredict platform using simulated user data? * _RQ3_: How well does the platform's architecture align with anticipated ethical, legal, and regulatory requirements, including the General Data Protection Regulation (GDPR), the Medical Device Regulation (MDR), and the emerging European AI Act? This paradigm is reflected in the modular architecture of the NeuroPredict platform, which facilitates multimodal monitoring in real-world settings, including data pertaining to speech, motor, cognitive, and emotional processes [3; 4; 5].