## 6 Conclusions and Future Work  
This paper presented a comprehensive and modular fall-detection framework designed to enhance the accuracy, efficiency, and explainability of monitoring systems for elderly care. At the core of our approach is the novel integration of short-time Fourier transform (STFT) for dynamic frame extraction, significantly reducing false alarms by focusing on frames exhibiting substantial motion. This targeted frame extraction is particularly beneficial in environments where continuous monitoring is essential yet computational efficiency is also a requirement. The framework includes a lightweight 1D convolutional neural network (CNN) optimized for low computational demand while maintaining high accuracy, making the system suitable for real-time applications in resource-constrained settings. Moreover, integrating gradient-weighted class activation mapping (GradCAM) provides valuable insights into the model's decision-making process, enhancing transparency and offering trustworthy feedback to caregivers and medical professionals.  
Despite these advancements, the system faces limitations, particularly in environments with complex dynamics, such as varying lighting conditions or physical obstructions that can obscure fall events. To address these challenges, future work will explore the integration of multimodal data inputs, such as combining visual data with other sensors like infrared or depth cameras. This approach aims to integrate more complex deep learning models to enhance detection robustness across diverse operational settings. Additionally, future studies will expand the dataset to include a broader spectrum of fall-related scenarios and diverse demographics to test the system's efficacy more comprehensively. We also plan to refine the GradCAM module to deliver more detailed visual explanations, facilitating a deeper understanding of the model's predictive behaviors.  
Overall, the proposed modular fall-detection framework represents a significant step forward in applying advanced machine learning techniques to elderly care. By enhancing the system's accuracy, reliability, and user trust, we aim to contribute to safer living environments for the elderly, ultimately reducing the incidence and impact of falls worldwide.  
All phases of this study, including conceptualization, methodology, software development, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, and visualization, were primarily executed by M.D. Supervision, writing--review and editing, and project administration were collaboratively handled by [PERSON], M.G. and C.W.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Norges Forskningsrad, grant number 310278.  
Not applicable.  
The datasets used in this study are the Multiple Cameras Fall Dataset (MCFD), available at [[https://www.iro.umontreal.ca/~labimage/Dataset/](https://www.iro.umontreal.ca/~labimage/Dataset/)]([https://www.iro.umontreal.ca/~labimage/Dataset/](https://www.iro.umontreal.ca/~labimage/Dataset/)) (accessed on 10 January 2024); the UR fall-detection dataset, available at [[http://fenix.ure.edu.pl/~mkepski/ds/uf.html](http://fenix.ure.edu.pl/~mkepski/ds/uf.html)]([http://fenix.ure.edu.pl/~mkepski/ds/uf.html](http://fenix.ure.edu.pl/~mkepski/ds/uf.html)) (accessed on accessed on 10 January 2024); and the Action Recognition Datasets \"NTU RGB+D\" and \"NTU RGB+D 120\", available at [[https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)]([https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)) (accessed on accessed on 10 January 2024). These are publicly available datasets, including the videos recorded, which were used to develop and validate our fall-detection approach.  
The authors declare no conflicts of interest.