Challenges and Trends in Multimodal Fall Detection for Healthcare

Hiram Ponce

ISBN 10: 303038747X / ISBN 13: 9783030387471

Published by Springer International Publishing Jan 2020, 2020

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 276 pp. Englisch.

Seller Inventory # 9783030387471

Bibliographic Details

• Title: Challenges and Trends in Multimodal Fall Detection for Healthcare
• Publisher: Springer International Publishing Jan 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 303038747X
• ISBN 13: 9783030387471
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 21D millimeters 241 millimeters width by 160 millimeters height by 21 millimeters depth
• Weight: 582 grams

About this title

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.