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

The Pre-VFall dataset is a multimodal dataset. It includes images, keygradient vector magnitude features, and keygradient vector direction features available to researchers for advancing the robustness of fall detection systems. The dataset is intended for use by the machine learning community to identify pattern cues that signal the onset of falls. The dataset provides new insights into how frailty states in older adults may serve as precursors to fall incidents. This will help improve the robustness of fall detection systems, ensuring they can effectively account for irregularities in movement and behavior that indicate early signs of fall. The dataset consists of around 22K images selected from recorded videos of nine healthy young adult participants. Each participant's videos and corresponding images are organized in folders named after each video session as follows: confusion_delirium, confusion_nph, dizzy_fall_forward, dizzy_fall_side, weakness_fall_forward, and weakness_fall_side. It should be noted that weakness and dizziness sessions were succeeded by falls and so included fall in their labels. The terms “forward” and “side” in some labels indicate the direction of fall. Each folder contains videos recorded with RGB cameras positioned at 90° and 45° with forward-view and side-view cameras included to identify the angle of the camera. The frames of the videos were manually selected and sorted into their respective categories. For example, the pre-fall activities such as weakness, dizziness, delirium-confusion, and NPH-confusion were categorized as “Abnormal,” while states of falling and actual falls were categorized as “Fall.” Therefore, this dataset encompasses three activity classes: normal, abnormal, and fall.Pre-VFall数据集是一个多模态数据集，包含图像、关键梯度向量幅值特征与关键梯度向量方向特征，可供研究人员用于提升跌倒检测系统的鲁棒性。本数据集面向机器学习社区，旨在识别预示跌倒发生的模式线索，同时为探究老年群体虚弱状态如何作为跌倒事件的前驱征兆提供了全新视角。这将助力提升跌倒检测系统的鲁棒性，确保其能够有效处理并识别预示跌倒早期征兆的运动与行为异常。本数据集包含约2.2万张图像，均选自9名健康青年受试者的录制视频。每位受试者的视频及对应图像均按照各视频时段命名的文件夹进行组织，具体文件夹名如下：confusion_delirium、confusion_nph、dizzy_fall_forward、dizzy_fall_side、weakness_fall_forward、weakness_fall_side。需说明的是，虚弱与眩晕时段均以跌倒事件收尾，因此其标签中包含"fall"字样；部分标签中的"forward"与"side"则表明跌倒的方向。每个文件夹均包含由90°与45°视角的RGB摄像机录制的视频，同时涵盖前视与侧视摄像机采集的视频，用于确定摄像机的拍摄角度。研究人员对视频帧进行了人工筛选，并将其归类至相应类别中。例如，跌倒前的活动（如虚弱、眩晕、谵妄-意识模糊以及正常压力脑积水（NPH）-意识模糊）被归类为"Abnormal"，而跌倒过程及实际跌倒状态则被归类为"Fall"。因此，本数据集涵盖三类活动类别：正常、异常与跌倒。

搜集汇总

背景与挑战

背景概述

Pre-VFall数据集是一个多模态数据集，包含图像、关键梯度向量幅度和方向特征，旨在通过机器学习识别跌倒的早期迹象，以提高跌倒检测系统的鲁棒性。数据集包括约22K图像，来自九名健康年轻成年参与者的视频，涵盖正常、异常（如虚弱和头晕）和跌倒三类活动类别，视频由不同角度的RGB摄像头记录，图像经过手动分类以支持跌倒预防研究。