Descriptor: Vision Sensor Simulated Early Signs of Fall Dataset (Pre-VFall)

Despite extensive research in machine learning for fall detection, early warning signs of falls have been largely overlooked. Current datasets mainly focus on fall mitigation rather than the irregularities in movement and behavior patterns that precede a fall, useful for fall prevention. Identifying these early signs is crucial for enabling timely interventions to reduce injury severity and improve the quality of life for older adults. To address this gap, we present the Pre-VFall dataset, a novel resource designed to simulate early fall indicators using vision sensor technology. Since RGB cameras already exist in common areas of living facilities for seniors, the proposed vision-based sensors become naturally well-suited. This open dataset comprises over 22K simulated instances encompassing normal conditions, various abnormal states (including weakness, dizziness, delirium-confusion, and Normal Pressure Hydrocephalus (NPH)-confusion), and fall events, all recorded from nine healthy young adult participants. The dataset includes comprehensive data in the form of videos, images, key gradient vector magnitude, and key gradient vector direction features. These elements are crucial for advancing research into the pre-fall irregularities that signal potential falls, thereby supporting the development of more sophisticated and proactive fall detection systems.