UMAFall: Fall Detection Dataset (Universidad de Malaga)

The files contain the mobility traces generated by a group of 19 experimental subjects that emulated a set of predetermined ADL (Activities of Daily Life) and falls. The traces are aimed at evaluating fall detection algorithms.Several video clips describing the performed movements are also included.The source and authors of this publicly available dataset should be acknowledged in all publications in which it is utilized as by referencing any of the following papers as well as this web-site: · - Santoyo-Ramón, José Antonio, Eduardo Casilari, and José Manuel Cano-García. "Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning." Sensors 18.4 (2018): 1155. · - Casilari, Eduardo, Jose A. Santoyo-Ramón, and Jose M. Cano-García. "UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection." Procedia Computer Science 110 (2017): 32-39.<br>本数据集文件包含19名实验受试者模拟一系列预设日常活动（Activities of Daily Life，以下简称ADL）与跌倒场景所生成的移动轨迹数据，此类轨迹旨在用于跌倒检测算法的评估。此外，数据集还附带了多段记录受试者完成相关动作的视频片段。所有使用本公开数据集的研究成果，均需对该数据集的来源及作者予以致谢，可通过引用下述任意一篇论文及本数据集官网进行标注： · - Santoyo-Ramón, José Antonio, Eduardo Casilari, 及 José Manuel Cano-García. 《基于智能手机多移动传感器架构的监督学习跌倒检测分析》，Sensors 18.4 (2018): 1155. · - Casilari, Eduardo, Jose A. Santoyo-Ramón, 及 Jose M. Cano-García. 《UMAFall：面向自动跌倒检测研究的多传感器数据集》，Procedia Computer Science 110 (2017): 32-39.

搜集汇总

背景与挑战

背景概述

UMAFall数据集是由19名实验对象生成的跌倒检测数据集，包含日常活动和跌倒的模拟数据以及相关视频片段，旨在评估跌倒检测算法。数据集由西班牙马拉加大学的研究人员创建，使用需引用相关论文和网站。