## An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
###### Abstract
While direct and personal, wearable sensors often suffer from discomfort, leading to non-compliance, limited battery life requiring frequent recharges, and the inability to accurately differentiate between actual falls and daily activities like sitting. Although video-based systems offer comprehensive monitoring, they raise significant privacy concerns and are subject to environmental factors such as poor lighting and obstructions, which can hinder their effectiveness. Additionally, these systems and other technologies, such as radar or acoustic sensors, can be costly and complex, posing barriers to widespread adoption. These limitations underscore the need for developing more adaptable, efficient, and privacy-conscious fall-detection systems. In addressing the challenges associated with video-based fall detection, our proposed approach integrates advanced machine learning techniques adapted to the specific needs of medical healthcare. This comprehensive architecture comprises several modular components: a Gaussian module for video obfuscation, the OpenPose library for pose estimation, a short-time Fourier transform (STFT) module for capturing motion-induced frames and generating temporal dynamics spectrograms, a classification module utilizing convolutional and dense layers, and a gradient-weighted class activation mapping (GradCAM) module for providing visual explanations of decision-making processes. The application of STFT in fall detection, particularly within video datasets, is relatively novel despite its extensive use in audio and speech analysis [15; 16]. STFT's ability to analyze the frequency content of signals across short time intervals allows for the detection of temporal changes, offering valuable insights into the dynamics and movement patterns associated with falls. Integrating STFT enhances the understanding of fall events. It is a crucial tool for healthcare professionals, helping them identify and analyze patterns and behaviors that lead to falls and improving patient care. Furthermore, our system incorporates the GradCAM technique, renowned for its explainability capabilities within computer vision. GradCAM effectively highlights the areas within an input image that significantly influence the decisions made by the neural network. By deploying GradCAM, we provide visual explanations that enhance the transparency of the fall-detection process, thereby allowing healthcare professionals to validate and trust the system's accuracy and reliability in detecting fall events. By integrating STFT and GradCAM, our approach merges the benefits of capturing detailed temporal dynamics and visualizing influential key points during falls. This dual integration facilitates a deeper understanding of the dynamics surrounding fall incidents and enables the precise analysis of crucial moments leading to falls. Additionally, the visual explanations provided by GradCAM significantly enhance the interpretability of the model's decisions, fostering trust and collaboration between healthcare providers and the fall-detection system. Our system's modular design allows customized configurations to meet diverse operational needs. For instance, in environments where privacy is not a primary concern, the Gaussian module can be deactivated to provide unobstructed video analysis. Conversely, in settings requiring continuous and comprehensive monitoring, the STFT module can be bypassed to allow uninterrupted data analysis. This flexibility ensures the system can be optimized for specific scenarios, enhancing its applicability and effectiveness. Key contributions of our approach include:  
* STFT-Based Motion Analysis: We introduce an innovative STFT application for fall detection that selectively processes only frames exhibiting significant motion. This targeted approach reduces the incidence of false alarms, a common challenge in continuous monitoring scenarios, thereby enhancing system reliability and efficiency.