Challenges and Trends in Multimodal Fall Detection for Healthcare

Hiram Ponce (u. a.)

ISBN 10: 303038747X / ISBN 13: 9783030387471

Editore: Springer, 2020

Challenges and Trends in Multimodal Fall Detection for Healthcare | Hiram Ponce (u. a.) | Buch | xiii | Englisch | 2020 | Springer | EAN 9783030387471 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 117867867

Riassunto:

<div><div><div>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.</div><div><br></div><div>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.</div><div>&nbsp;<br></div><div>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.</div></div></div><div><br></div>

Dalla quarta di copertina: <div><div>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.</div><div>&nbsp;<br></div><div>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.</div><div>&nbsp;<br></div><div>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.</div></div><div><br><br></div>

Dati bibliografici

Titolo: Challenges and Trends in Multimodal Fall ...
Casa editrice: Springer
Data di pubblicazione: 2020
Legatura: Buch
Condizione: Neu