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>本数据集包含的文件涵盖19名实验受试者生成的移动轨迹数据，受试者模拟了一系列预设的日常活动（Activities of Daily Life，ADL）与跌倒场景。该轨迹数据旨在用于跌倒检测算法的性能评估。此外，数据集还附带了多段记录上述动作执行过程的视频片段。 本公开数据集的来源与作者需在所有使用该数据集的学术出版物中予以致谢，可通过引用以下任意一篇论文及本数据集的官方网站完成标注： · Santoyo-Ramón, José Antonio, Eduardo Casilari, 及 José Manuel Cano-García. 《基于多移动传感器的智能手机架构实现监督学习跌倒检测的分析》，发表于《Sensors》2018年第18卷第4期，页码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》，发表于《Procedia Computer Science》2017年第110卷，页码32-39。