## 2 Related Work
### Shortcomings of Existing Approaches and NeuroPredict Platform's Anticipated Added Value  
Several frequent constraints of a great number of related digital health solutions are shown by a thorough examination of current digital health systems aimed at managing neurodegenerative diseases. First, many solutions have limited functionality dimensions, concentrating on distinct cognitive tests, physiological data collection, or gait monitoringwith no incorporation of all these elements into a single, time-sensitive architecture. Either interpretation of circumstances or predictability is hampered by this disjointed approach. Also, the overwhelming majority of analyzed solutions make use of static classification algorithms or threshold-based detection logic, which are not properly adaptable to the diversity of individual patients. The systems' capacity to detect early changes in functional status is diminished by the absence of customized initial conditions and the insufficient use of time-series modeling. Translation into clinical practice and regulatory compliance is hindered by the frequently inadequate handling of algorithmic transparency and lifecycle monitoring.  
With a multilayered architecture that integrates motion data, physiological signals, and cognitive performance measurements, the NeuroPredict platform has been designed to make up for these shortcomings. Featuring an emphasis on temporal feature extraction and multimodal correlation, machine learning techniques that can identify anomalies in real-time and model progression are used. The NeuroPredict platform encompasses ethical and regulatory compliance--GDPR, MDR, and AI Act considerations--at the system design level, which sets it apart from other similar digital health solutions. Such features as risk-aware AI governance approaches, consent management infrastructure, and data minimization--all of which are not frequently employed in similar platforms. The NeuroPredict platform proposes a distinctive structure for ongoing, responsive, and ethically responsible monitoring of neurodegenerative diseases in nonclinical settings by overcoming both functional and regulatory shortcomings noted in the available research.  
In contrast, only about 3% of the extensive research on inertial sensors for Parkinson's disease demonstrates clinical utility, with most efforts still in the technical validation stage [55]. The NeuroPredict platform provides a clinically relevant monitoring architecture designed for real-world implementation. While platforms such as Kinesia [56] and HopkinsPD rely solely on smartphones to track activity (achieving ~71% accuracy in detecting medication response), they lack multimodal integration, versioned AI pipelines, and embedded regulatory compliance. The JTrack platform, with its GDPR alignment, marks progress in digital biomarker collection, yet remains limited to passive smartphone data and lacks customizable configurations [57]. Moreover, despite comprehensive EU regulations on AI and healthcare, few platforms integrate regulatory frameworks directly into system design--one of NeuroPredict's core advantages [58].  
While earlier platforms have addressed isolated aspects of neurodegenerative disease management, NeuroPredict uniquely combines multimodal sensing, a regulation-by-design architecture, and explainable machine learning within an integrated, modular framework, an approach rarely implemented in existing solutions.