Table of contents
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.
Fall Detection Fall Classification Human Fall Detection Fall Detection data Set Intelligent Real-Time Multimodal Fall Detection
Editors and affiliations
- Book Title Challenges and Trends in Multimodal Fall Detection for Healthcare
- Series Title Studies in Systems, Decision and Control
- Series Abbreviated Title Studies in Systems, Decision and Control
- 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 Engineering (R0)
- Hardcover ISBN 978-3-030-38747-1
- Softcover ISBN 978-3-030-38750-1
- eBook ISBN 978-3-030-38748-8
- Series ISSN 2198-4182
- Series E-ISSN 2198-4190
- Edition Number 1
- Number of Pages XIII, 259
- Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
Biomedical Engineering and Bioengineering
- Buy this book on publisher's site