Challenges and Trends in Multimodal Fall Detection for Healthcare (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Systems, Decision and Control, 273
Release date:	2020
First published:	2020
Editors:	Hiram Ponce • Lourdes Martinez-Villasenor • Jorge Brieva • Ernesto Moya-Albor
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	259
Edition:	1st ed. 2020
ISBN-13:	978-3-03-038747-1
Categories:	Books > Medicine > Nursing & ancillary services > Biomedical engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-038747-X
Barcode:	9783030387471

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