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Challenges and Trends in Multimodal Fall Detection for Healthcare

  • Hiram Ponce
  • Lourdes Martínez-Villaseñor
  • Jorge Brieva
  • Ernesto Moya-Albor
Book

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

Table of contents

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

    1. Front Matter
      Pages 1-1
    2. Hiram Ponce, Lourdes Martínez-Villaseñor, José Núñez-Martínez, Ernesto Moya-Albor, Jorge Brieva
      Pages 3-29
    3. Silvano Ramos de Assis Neto, Guto Leoni Santos, Elisson da Silva Rocha, Malika Bendechache, Pierangelo Rosati, Theo Lynn et al.
      Pages 31-51
    4. Hristijan Gjoreski, Simon Stankoski, Ivana Kiprijanovska, Anastasija Nikolovska, Natasha Mladenovska, Marija Trajanoska et al.
      Pages 81-96
    5. Ricardo Espinosa, Hiram Ponce, Sebastián Gutiérrez, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor
      Pages 97-120
  3. Reviews and Trends on Multimodal Healthcare

    1. Front Matter
      Pages 135-135
    2. Paulo Vitor C. Souza, Augusto J. Guimaraes, Vanessa S. Araujo, Lucas O. Batista, Thiago S. Rezende
      Pages 181-205
    3. Juana Isabel Méndez, Omar Mata, Pedro Ponce, Alan Meier, Therese Peffer, Arturo Molina
      Pages 207-235
    4. Yoosuf Nizam, M. Mahadi Abdul Jamil
      Pages 237-259

About this book

Introduction

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.


Keywords

Fall Detection Fall Classification Human Fall Detection Fall Detection data Set Intelligent Real-Time Multimodal Fall Detection

Editors and affiliations

  • Hiram Ponce
    • 1
  • Lourdes Martínez-Villaseñor
    • 2
  • Jorge Brieva
    • 3
  • Ernesto Moya-Albor
    • 4
  1. 1.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico
  2. 2.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico
  3. 3.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico
  4. 4.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-38748-8
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-38747-1
  • Online ISBN 978-3-030-38748-8
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site