## 4 Discussion
Moreover, compliance with those laws would guarantee that activities of data storing, processing, and sharing are compatible with the legal framework of data protection;
5. **Anonymization and Pseudonymization:** Where personal identifiers have to be held for clinical reasons, anonymization or pseudonymization techniques must be used. Anonymization wipes out identifying information from the data set in a way that cannot be reversed, so that it is no longer feasible to link data with an individual. Pseudonymization, by comparison, entails substituting identifying information with pseudonyms such that the data may be linked to a person under certain conditions with approved access only. Although the data sets used in this work were pre-collected and anonymized, clinical use of this model will have to adhere to stringent data protection and privacy laws. Future work will be necessary to incorporate robust encryption methods, secure access controls, and clear patient consent procedures to ensure that actual clinical settings using such models align with legal and ethical requirements, both safeguarding patient confidentiality and model integrity. **Limitations and Future Validation in Clinical Contexts**  
While this study achieves a sufficient demonstration of a robust and generalizable proof of concept with open-source datasets, we need to acknowledge that open-source datasets never completely reflect the real-world clinical setting complexity and variability. Therefore, the reported performance metrics and outcomes here should be viewed as foundational but not necessarily absolute. Confirmation in the future will depend on demonstrating the proposed fusion strategies within actual clinical workflows, that is, within dementia units, where the behavioral events are subtle, context-dependent, and affected by hundreds of confounding factors such as medication, cognitive impairment, and social relationships. The objective will be to validate the performance of the model on real clinical video and audio data with proper ethical approvals and data governance frameworks. Furthermore, longitudinal data collection will be accorded priority to account for intra-individual behavioral variation over time. These are necessary steps to progress from a simulated evaluation environment to a clinically validated assistive technology that can facilitate frontline caregivers in high-stakes decision-making reliably. In this research, the findings indicate that in this proof of concept of a novel model for predicting episodes of aggressive behavior in dementia patients, Late Fusion is the best choice due to its robustness and effective approach with a superior recall, accuracy, and overall performance. Its advantages make it the best choice for applications where clinical caregivers and institutions must accurately and timely intervene to prevent such scenarios. However, the Early Fusion technique remains a solid alternative for scenarios where the avoidance of false positive alarms is the main concern in unsupervised applications. Future research should focus on implementing and testing real clinical data (**S1.3**) from partnerships with clinical institutions and shed further light on the results or the need for optimized fusion strategies by exploring hybrid approaches that combine the strengths of each methodology.