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UP-Fall Detection Dataset: A Multimodal Approach.

Lourdes Martínez-Villaseñor1, Hiram Ponce2, Jorge Brieva3

  • 1Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, México, Ciudad de México 03920, Mexico. lmartine@up.edu.mx.

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Summary
This summary is machine-generated.

This study introduces the UP-Fall Detection Dataset, a comprehensive resource for evaluating fall detection systems. It enables fair comparisons of machine learning techniques for human activity recognition and fall prevention.

Keywords:
fall detectionhealthcaremachine learningmultimodal datasetvision

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Gerontology

Background:

  • Falls pose a significant global health risk, particularly for the elderly.
  • Effective fall detection systems are crucial for mitigating fall-related injuries.
  • A lack of standardized datasets hinders fair comparison of fall detection algorithms.

Purpose of the Study:

  • To introduce the UP-Fall Detection Dataset for advancing fall detection research.
  • To facilitate standardized benchmarking of machine learning models for fall detection.
  • To support research in human activity recognition using diverse sensor data.

Main Methods:

  • Collected data from 17 healthy young individuals performing 11 activities and falls.
  • Utilized wearable sensors, ambient sensors, and vision devices.
  • Compiled over 850 GB of raw and feature datasets.

Main Results:

  • The dataset includes detailed activity and fall instances with multiple attempts.
  • It offers a large-scale, multi-modal data repository.
  • Two experimental use cases demonstrate dataset utility.

Conclusions:

  • The UP-Fall Detection Dataset provides a valuable resource for the research community.
  • It enables fair and reproducible evaluation of fall detection systems.
  • Facilitates advancements in signal recognition, vision, and machine learning for fall prevention.