## 3 Materials and Methods
### Ethical Principles, GDPR Compliance, and Data Security  
The NeuroPredict platform was developed in accordance with internationally recognized legal and ethical frameworks, thereby ensuring its compliance and suitability for deployment in sensitive healthcare contexts. Adherence to fundamental principles of data ethics provides a solid and responsible foundation for the development and application of artificial intelligence technologies [44; 45; 66; 67]. From the very beginning of design to the anticipated clinical deployment, the NeuroPredict platform's ethical-regulatory development is directed by a systematic, multiphase pipeline that incorporates ethical protections and legal compliance. To ensure that regulatory requirements are integrated into the technical lifecycle rather than applied retroactively, each phase addresses distinct aspects of GDPR, MDR, and AI Act alignment. This step-by-step integration process is depicted in Figure 5.  
Figure 4: The workflow for collecting real-time acceleration data using GaitBand and processing it for machine learning algorithms. Figure 5: Ethical-regulatory development pipeline of the NeuroPredict platform, showing the seven phases from conceptualization to clinical deployment. This process includes:  
1. **Conception and Planning**: The design phase incorporated fundamental ethical principles, such as informed consent, user autonomy, and privacy. The platform's original architecture and data flows were influenced by anticipated GDPR and MDR compliance. 2. **Document and Policy Development**: Data use policies that clearly connect user rights to system functionalities were developed, along with consent forms specifically designed for patients and clinicians. 3. **Integration into Platform Design**: The interface now includes digital informed consent mechanisms. To facilitate auditability and safe data handling, session-level logging, role-based access, encryption, and pseudonymization were enabled. 4. **Development and Testing**: User session simulations confirmed that ethical standards (such as assigning consent to data records, limiting access, and encrypting transmission) were appropriately implemented. Iterative updates were informed by ethics reviewers' and developers' feedback. 5. **Controlled Deployment**: Internal audits verified that privacy measures like metadata tracking, secure upload protocols, and AI inference logging were implemented correctly. Internal training procedures and regulatory documentation were started. To guarantee the traceability and explainability of algorithm outputs, specific checkpoints for MDR and AI Act readiness were established. 6. **Clinical Deployment Preparation**: Participants' actual informed consent will be gathered, and ethical precautions will be continuously observed. Stakeholder feedback will be used to continuously improve platform updates. Ethical and legal compliance is guaranteed to be an ingrained design principle rather than a post-hoc fix thanks to this lifecycle-based governance. Through trace logs and a conceptual DPIA conducted in compliance with GDPR Article 35, the platform operationalizes all the fundamental GDPR principles (Table 3), including explicit consent, the right of withdrawal, pseudonymization, data minimization, and auditability [45; 66]. The NeuroPredict platform directly integrates privacy-by-design principles into its data architecture to guarantee adherence to GDPR and MDR standards. Using a session-level tokenization system, all personal identifiers are pseudonymized at the time of acquisition