ARCHIVE
videos_ADL.zip (64.19 MB)
ARCHIVE
videos_FALL.zip (17.38 MB)
ARCHIVE
UMAFall_Dataset.zip (74.92 MB)
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UMAFall: Fall Detection Dataset (Universidad de Malaga)
Version 7 2018-06-04, 14:12
Version 6 2017-05-23, 10:43
Version 5 2017-05-02, 10:33
Version 4 2017-02-17, 13:00
Version 3 2016-11-08, 09:45
Version 2 2016-11-08, 09:33
Version 1 2016-11-08, 09:30
dataset
posted on 2018-06-04, 14:12 authored by Eduardo CasilariEduardo Casilari, Jose A.Santoyo-RamónThe
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.
