Development of an advanced thermal imaging-based human fall detection system

Abstract

Falls represent a significant risk to the elderly population, often leading to severe injuries or fatalities. Automatic fall detection systems (FDS) are critical for mitigating these risks; however, existing solutions, despite reporting accuracies in controlled environments, often fail to generalize to real-world conditions. This performance gap stems from limitations in existing datasets, overfitted models, and a lack of standardization. To address these challenges, this thesis presents a comprehensive framework for fall detection, leveraging privacy-preserving thermal imaging to develop deployable, real-world solutions. The research is conducted in three progressive phases. The first phase explores a novel hybrid architecture that combines supervised and unsupervised learning paradigms through a stacking ensemble of 3D Convolutional Neural Networks (3D CNNs) and Autoencoders (AEs). This hybrid approach demonstrates significant performance improvements on constrained datasets, highlighting its potential in scenarios where fully supervised methods fall short. Ablation studies validate the architecture’s utility while underscoring the critical need for a more robust dataset to achieve true generalizability. In the second phase, the thesis introduces Thermal Fall 66 (TF-66), the most diverse and comprehensive thermal fall detection dataset to date. Designed to address the limitations identified in Phase 1, TF-66 encompasses varied environments, participant demographics, and fall scenarios. Accompanied by targeted subsets and flexible data generators, TF-66 serves as a benchmark for meaningful comparisons and standardized evaluations, advancing the field toward real-world applicability. The third phase synthesizes insights from the hybrid approach and TF-66 dataset to refine a supervised 3D CNN model. Enhanced with innovative features such as optical flow integration and attention mechanisms, this model achieves state-of-the-art performance on TF-66 and the widely used Thermal Simulated Fall (TSF) dataset. By bridging the gap between lab-optimized systems and real-world demands, this final phase establishes a transformative approach to fall detection, redefining the state of fall detection research, with a focus on generalizable systems that can operate in real-time. The findings provide a clear path for developing accurate, privacy-preserving, and scalable fall detection systems, ultimately aiming to enhance the safety and save lives of seniors worldwide.