Challenges and Trends in Multimodal Fall Detection for Healthcare

1. Auflage 2020
ISBN: 978-3-030-38747-1
Verlag: Springer International Publishing

Buch, Englisch, 259 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 582 g

Reihe: Studies in Systems, Decision and Control

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.

Zielgruppe

Research

Autoren/Hrsg.

Ponce, Hiram

Moya-Albor, Ernesto

Martínez-Villaseñor, Lourdes

Brieva, Jorge

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Biomechanik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Challenges and Solutions on Human Fall Detection and Classi?cation.- Open Source Implementation for Fall Classi?cation and Fall Detection Systems.- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study.- Approaching Fall Classi?cation using the UP-Fall Detection Dataset: Analysis and Results from an International Competition.- Reviews and Trends on Multimodal Healthcare.- A Novel Approach for Human Fall Detection and Fall Risk Assessment.