{"url":"/dataset/urfd-dataset","name":"URFD Dataset","full_name":"UR Fall Detection Dataset","description_markdown":"This dataset contains 70 (30 falls + 40 activities of daily living) sequences. Fall events are recorded with 2 Microsoft Kinect (RGB + Depth) cameras and corresponding accelerometric data. ADL events are recorded with only one camera and accelerometer. Sensor data was collected using PS Move (60Hz) and x-IMU (256Hz) devices.","description_withheld":null,"homepage":"http://fenix.ur.edu.pl/~mkepski/ds/uf.html","introduced_date":"2014-10-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/human-fall-detection-on-embedded-platform","title":"Human fall detection on embedded platform using depth maps and wireless accelerometer","first_author":"Bogdan Kwolek","url":null},"license":null,"modalities":[{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"},{"name":"Action Detection","url":"/task/action-detection","datasets_with_task":"/datasets/task/action-detection"}],"languages":[],"variants":["URFD Dataset"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}