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Challenges and Trends in Multimodal Fall Detection for Healthcare

  • Book
  • © 2020

Overview

Editors:
  1. Hiram Ponce

    1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

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  2. Lourdes Martínez-Villaseñor

    1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

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  3. Jorge Brieva

    1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

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  4. Ernesto Moya-Albor

    1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

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  • Covers challenging issues and current trends for designing fall detection systems using a multimodal approach
  • Provides novel implementations of sensor technologies, artificial intelligence, machine learning, and statistics for fall detection systems
  • Describes and discusses a common, public dataset, especially gathered for multimodal fall detection

Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 273)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Challenges and Solutions on Human Fall Detection and Classification

    1. Front Matter

      Pages 1-1
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    2. Open Source Implementation for Fall Classification and Fall Detection Systems

      • Hiram Ponce, Lourdes Martínez-Villaseñor, José Núñez-Martínez, Ernesto Moya-Albor, Jorge Brieva
      Pages 3-29

    3. Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

      • Silvano Ramos de Assis Neto, Guto Leoni Santos, Elisson da Silva Rocha, Malika Bendechache, Pierangelo Rosati, Theo Lynn et al.
      Pages 31-51

    4. Intelligent Real-Time Multimodal Fall Detection in Fog Infrastructure Using Ensemble Learning

      • V. Divya, R. Leena Sri
      Pages 53-79

    5. Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognition

      • Hristijan Gjoreski, Simon Stankoski, Ivana Kiprijanovska, Anastasija Nikolovska, Natasha Mladenovska, Marija Trajanoska et al.
      Pages 81-96

    6. Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

      • Ricardo Espinosa, Hiram Ponce, Sebastián Gutiérrez, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor
      Pages 97-120

    7. Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition

      • Hiram Ponce, Lourdes Martínez-Villaseñor
      Pages 121-133

  3. Reviews and Trends on Multimodal Healthcare

    1. Front Matter

      Pages 135-135
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    2. Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

      • Yoosuf Nizam, M. Mahadi Abdul Jamil
      Pages 137-179

    3. An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

      • Paulo Vitor C. Souza, Augusto J. Guimaraes, Vanessa S. Araujo, Lucas O. Batista, Thiago S. Rezende
      Pages 181-205

    4. Multi-sensor System, Gamification, and Artificial Intelligence for Benefit Elderly People

      • Juana Isabel Méndez, Omar Mata, Pedro Ponce, Alan Meier, Therese Peffer, Arturo Molina
      Pages 207-235

    5. A Novel Approach for Human Fall Detection and Fall Risk Assessment

      • Yoosuf Nizam, M. Mahadi Abdul Jamil
      Pages 237-259
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Keywords

About this book

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion.


It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples.
 

This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.




Editors and Affiliations

  • Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor

Bibliographic Information

  • Book Title: Challenges and Trends in Multimodal Fall Detection for Healthcare

  • Editors: Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor

  • Series Title: Studies in Systems, Decision and Control

  • DOI: https://doi.org/10.1007/978-3-030-38748-8

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-38747-1Published: 29 January 2020

  • Softcover ISBN: 978-3-030-38750-1Published: 29 January 2021

  • eBook ISBN: 978-3-030-38748-8Published: 28 January 2020

  • Series ISSN: 2198-4182

  • Series E-ISSN: 2198-4190

  • Edition Number: 1

  • Number of Pages: XIII, 259

  • Topics: Biomedical Engineering and Bioengineering, Computational Intelligence, Biomechanics

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