## An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection
###### Abstract  
Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system's explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB+D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results. classification module; computational efficiency; deep learning; fall detection; gradient-weighted class activation mapping: OpenPose; privacy preservation; real-time monitoring; short-time Fourier transform; video-based systems +
Footnote †: journal:  
Video-based fall-detection algorithms have gained significant attention due to their potential for real-time assistance and comprehensive monitoring capabilities. Using video datasets allows one to capture detailed spatiotemporal information and enables the development of advanced machine learning models [7]. Recent research has shown promising results in using video-based approaches, including deep-learning-based methods [8] and ensemble learning techniques [9], to improve accuracy and robustness in fall detection. However, researchers must address several challenges to ensure video-based fall-detection systems' widespread acceptance and effectiveness. Privacy preservation is critical when working with video data as they often contain sensitive information about individuals [10]. Data protection regulations, such as the General Data Protection Regulation (GDPR), impose strict requirements for collecting, storing, and processing personal data [11]. Ensuring privacy while maintaining the effectiveness of fall-detection algorithms is challenging. Integrating privacy-preserving techniques, such as video obfuscation or blurring, becomes essential to balance privacy and fall-detection accuracy [12]. Another drawback of traditional machine learning models is their lack of transparency, operating as \"black boxes\" where the decision-making process is not easily interpretable [13]. This lack of interpretability can be a significant challenge, particularly in medical applications such as fall detection, where healthcare professionals need to understand the reasoning behind the detection outcomes to trust the system [14]. Healthcare professionals must have insight into how the algorithm arrives at its decisions in healthcare settings as this helps them validate and understand the results. The explainability of fall-detection algorithms is essential for building trust, ensuring patient safety, and facilitating effective collaboration between the algorithm and healthcare providers. Existing fall-detection solutions face several challenges.