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Fall-related injuries pose a significant health risk, particularly among the elderly population, necessitating advancements in fall detection technologies. While traditional sensor-based methods offer some solutions, they are limited by issues, like user compliance and unreliability in high-stakes situations. Computer vision (CV) and artificial intelligence (AI)-based alternatives, notably thermal imaging, emerge as promising, privacy-preserving tools. However, the lack of standardized, generalizable benchmark datasets hampers the validation of these technologies’ effectiveness. The Thermal Fall 66 (TF-66) dataset introduced in this work addresses this crucial gap by providing an extensive, diverse collection of fall scenarios, recorded in various environments and featuring a wide range of participants. This dataset introduces sample subsets for tailored model training and a comprehensive data generator for customized access, setting a new benchmark in fall detection research. A baseline three-dimensional convolutional neural network (3D CNN) was trained to demonstrate the dataset’s suitability for supervised deep learning. As TF-66 continues to evolve, it aims to include even more diverse environments, such as bathrooms and staircases, further enhancing its applicability and serving as a robust resource for the research community. This work contributes significantly to the field by offering a dataset that not only improves the reliability of fall detection systems (FDS) but also facilitates a standardized approach to evaluating and advancing these technologies.
Silver et al. (Tue,) studied this question.