Overview
- 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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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.
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.
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Keywords
Table of contents (10 chapters)
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Front Matter
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Challenges and Solutions on Human Fall Detection and Classification
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Front Matter
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Reviews and Trends on Multimodal Healthcare
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Front Matter
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Editors and Affiliations
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