## 5 Conclusions  
The results achieved through the proposed fall detection and subject anonymization method, as tested on the LE2I and URFD datasets, demonstrate the system's efficacy in ensuring privacy while maintaining high accuracy in fall detection across various real-world scenarios. The method's performance, with a frame-wise accuracy reaching 98.86% and a precision of up to 97.18%, alongside the video-wise accuracy and precision both reaching 100%, demonstrates its robustness and reliability. Particularly noteworthy is the system's specificity, which significantly reduces false alarms, a crucial factor in elderly monitoring systems to prevent unnecessary panic or stress. The subject anonymization aspect of this study plays a pivotal role in addressing privacy concerns, a significant barrier to the acceptance of monitoring technologies among elderly people. By effectively anonymizing individuals in the video through the removal of identifiable features and replacing them with 2D avatars, this method respects the privacy of the monitored individuals. This approach not only ensures the utility of the monitoring system for safety and wellness purposes but also promotes its acceptance among elderly users who might be concerned about their privacy and dignity. In conclusion, while current state-of-the-art technologies in fall detection offer high accuracy, they often require substantial computational resources and typically overlook the ethical and user-centric aspects of monitoring. This lack of attention to user preferences, particularly around visual privacy, can significantly hinder the acceptance and applicability of these technologies among elderly users, who may prioritize dignity and privacy in their care solutions. This study offers a robust solution that effectively balances the dual objectives of accurate fall detection and thorough privacy protection. The high accuracy and low false-positive rate indicate significant progress in elderly care technology. Importantly, this research highlights the innovative application of artificial intelligence not just for utility in fall detection but also in addressing design needs related to privacy concerns. Its emphasis on privacy protection through subject anonymization is likely to encourage greater acceptance among the elderly population, making it a significant step forward in the development of assistive technologies that cater to the sensitive needs of this demographic. Some of the limitations observed in this study are inherent to using 2D avatar representations to model complex 3D human movements. While these 2D avatars are effective for basic monitoring, they struggle to accurately convey complex actions from certain angles, which can lead to ambiguities in interpreting the subject's actual posture. Future research could focus on integrating optimized 3D modeling techniques, which offer a more detailed and dynamic representation of human poses while preserving real-time performance. Future work will also focus on refining the fall detection and anonymization algorithms for enhanced accuracy and real-time processing, exploring multi-camera setups to reduce occlusions, and integrating advanced privacy-preserving techniques. Additionally, efforts will aim at improving user acceptance through ethical considerations to ensure that the technology not only meets the highest standards of efficiency and privacy but also considers the major aspects of social ethics. Conceptualization, C.-Y.W. ; methodology, C.-Y.W. ; software, C.-Y.W. ; validation, C.-Y.W. and F.-S.L. ; formal analysis, C.-Y.W. ; investigation, C.-Y.W. ; resources, C.-Y.W. ; data curation, C.-Y.W. ; writing--original draft preparation, C.-Y.W. ; writing--review and editing, F.-S.L. ;visualization, C.-Y.W. ; supervision, F.-S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.