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(multimodal dataset)。它包含图像、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。本数据集旨在供机器学习社区使用，以识别预示跌倒发生的模式特征线索。该数据集为探究老年人衰弱状态如何作为跌倒事件的前驱影响因素提供了全新视角，这将有助于提升跌倒检测系统的鲁棒性，确保其能够有效覆盖指示跌倒早期迹象的运动与行为异常情况。本数据集包含约2.2万张图像，均选自9名健康年轻成年受试者的录制视频。每位受试者的视频及对应图像均按视频会话所在文件夹进行组织，文件夹名称如下：confusion_delirium、confusion_nph、dizzy_fall_forward、dizzy_fall_side、weakness_fall_forward、weakness_fall_side。需注意，衰弱与眩晕会话后续均伴随跌倒事件发生，因此其标签中包含"fall"字样。部分标签中的"forward"与"side"用于指示跌倒的方向。每个文件夹均包含由90°与45°视角RGB摄像机录制的视频，同时配备前视与侧视摄像机以确定拍摄角度。视频帧均经过人工筛选并归类至相应类别。例如，跌倒前的活动（如衰弱、眩晕、谵妄-意识模糊以及正常压力脑积水(Normal Pressure Hydrocephalus, NPH)相关意识模糊）被归类为"异常"类别，而跌倒过程与实际跌倒事件则被归类为"跌倒"类别。因此，本数据集涵盖三类活动类别：正常、异常与跌倒。