## 5 Conclusions  
This study presents a comparative analysis of Early and Late Fusion models for aggression detection in dementia patients using multimodal audio-visual data as a novel proof-of-concept methodology. According to the findings, Late Fusion outperforms Early Fusion in most key metrics, including accuracy (0.876 vs. 0.828), recall (0.914 vs. 0.818), F1-score (0.867 vs. 0.835), and ROC-AUC (0.970 vs. 0.922), making it more effective in detecting aggressive behaviors. Its higher recall suggests better detection of aggressive incidents, making it suitable for high-risk applications, while its faster inference times enhance its potential for real-time use. In contrast, Early Fusion achieved higher precision (0.852 vs. 0.824), indicating a lower false positive rate, which may be beneficial in scenarios where minimizing false alarms is crucial. Additionally, Late Fusion's superiority is reinforced by its higher Balanced Accuracy (0.880 vs. 0.829) and Cross-Validated ROC-AUC (0.959 vs. 0.924), suggesting better generalizability. **Future Directions**  
This study provides results that open new opportunities for further advancements in human behavioral prediction models. It also paves the road for deep learning incorporation techniques for feature extraction that could improve the classification accuracy, such as CNNs and RNNs. The latest could be explored for a more complex approach in the visual and audio patterns. Furthermore, hybrid fusion strategies that integrate certain aspects from both Early and Late Fusion techniques could provide an even more robust solution by integrating the strengths from each approach. This would provide increased accuracy and low false positive rates while maintaining the superior classification capabilities, providing a reliable detection system. Finally, attention mechanisms could be implemented during the fusion process to refine the decision-making ability of the model. This will allow for a more dynamic weight-critical feature mechanism and improve even more the classification performance. While this research presents a strong way of identifying violence in terms of postures and gestures, there are still various directions for further development. One direction is the granularity of distinguishing between actions. Our system currently distinguishes violent from non-violent action but might, in future follow-up work, continue to distinguish action further to identify the more subtle domains of aggression involving physical aggression, verbal aggression, or self-injury. This would not merely enhance the ability of the model to react with greater accuracy against different types of violent conduct, but also provide more specific indications of actual-world intervention. On top of this, fine-grained action identification would help disambiguate between unlike intensities of aggressiveness, paving the way for more targeted action in public safety settings. Deployability in the actual world is another key dimension for enhancement. Although the system has been tuned for real-time performance, precision is impacted by factors such as low-resolution video streams, occlusions, or adverse environmental conditions (e.g., low illumination, high-density environments, or high-speed motion). The aforementioned problems may be solved with data augmentation techniques, state-of-the-art pose detection algorithms, and multimodal fusion (e.g., combining audio or thermal data with visual data) that might boost the system's resilience in uncontrolled and dynamic environments. Secondly, adaptive learning techniques could be employed to allow the model to continuously improve its detection abilities by adding new data in varied environments and conditions so that it becomes more suitable to generalize and adapt to variable conditions. All these advancements would significantly improve the system's uses for real-time surveillance, and it would provide a more general solution for detecting violence in various real-world applications.