Evaluating and Enhancing Radar-Based Fall Detection with Diffusion-Generated Synthetic Data

Radar-based fall detection systems offer significant potential to enhance the safety and quality of life for individuals with Alzheimer's Disease and Alzheimer's Disease-Related Dementias (AD/ADRD). These systems have demonstrated impressive accuracy when evaluated on standardized datasets; however, real-world deployment often reveals a marked drop in performance due to the challenges posed by environmental complexity, clutter, and variability in human behavior. This study explores two primary research questions: firstly, assessing the realism and transferability of performance metrics from standardized datasets to practical, cluttered environments; secondly, determining if and to what extent performance in realistic settings can be improved by augmenting datasets with synthetically generated falls and activities of daily life data using a U-Net diffusion model. Our findings highlight substantial performance gaps between standardized datasets and realistic conditions. Preliminary experiments demonstrate that introducing generated fall data can significantly enhance detection accuracy in practical settings, providing insights into the optimal amount of synthetic data needed to maximize detection effectiveness.