Challenges and Trends in Multimodal Fall Detection for Healthcare (1st ed. 2020) (Studies in Systems, Decision and Control #273)

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Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9783030387488
Related ISBNs:
9783030387471
Publisher:
Springer International Publishing
Date of Addition:
Copyrighted By:
Springer
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Science, Sports, Technology, Medicine
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.
Edited by:
Ernesto Moya-Albor
Edited by:
Jorge Brieva
Edited by:
Lourdes Martínez-Villaseñor
Edited by:
Hiram Ponce

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