Sensors (Apr 2019)

UP-Fall Detection Dataset: A Multimodal Approach

  • Lourdes Martínez-Villaseñor,
  • Hiram Ponce,
  • Jorge Brieva,
  • Ernesto Moya-Albor,
  • José Núñez-Martínez,
  • Carlos Peñafort-Asturiano

DOI
https://doi.org/10.3390/s19091988
Journal volume & issue
Vol. 19, no. 9
p. 1988

Abstract

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Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.

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