Challenges and Trends in Multimodal Fall Detection for Healthcare

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

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.

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

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

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

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