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Challenges and trends in multimodal ...

Ponce, Hiram.

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Challenges and trends in multimodal fall detection for healthcare

Record Type:	Electronic resources : Monograph/item
Title/Author:	Challenges and trends in multimodal fall detection for healthcare/ edited by Hiram Ponce ... [et al.].
other author:	Ponce, Hiram.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiii, 259 p. :ill., digital ;24 cm.
[NT 15003449]:	Challenges and Solutions on Human Fall Detection and Classiﬁcation -- Open Source Implementation for Fall Classiﬁcation and Fall Detection Systems -- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study -- Approaching Fall Classiﬁcation using the UP-Fall Detection Dataset: Analysis and Results from an International Competition -- Reviews and Trends on Multimodal Healthcare -- A Novel Approach for Human Fall Detection and Fall Risk Assessment.
Contained By:	Springer eBooks
Subject:	Biosensors. -
Online resource:	https://doi.org/10.1007/978-3-030-38748-8
ISBN:	9783030387488

Challenges and trends in multimodal fall detection for healthcare
Challenges and trends in multimodal fall detection for healthcare[electronic resource] /edited by Hiram Ponce ... [et al.]. - Cham :Springer International Publishing :2020. - xiii, 259 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.2732198-4182 ;. - Studies in systems, decision and control ;v.273..

Challenges and Solutions on Human Fall Detection and Classiﬁcation -- Open Source Implementation for Fall Classiﬁcation and Fall Detection Systems -- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study -- Approaching Fall Classiﬁcation using the UP-Fall Detection Dataset: Analysis and Results from an International Competition -- Reviews and Trends on Multimodal Healthcare -- A Novel Approach for Human Fall Detection and Fall Risk Assessment.

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.

ISBN: 9783030387488

Standard No.: 10.1007/978-3-030-38748-8doiSubjects--Topical Terms:

621001
Biosensors.

LC Class. No.: R857.B54 / C435 2020

Dewey Class. No.: 610.285

Challenges and trends in multimodal fall detection for healthcare
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490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.273
505 0 $a Challenges and Solutions on Human Fall Detection and Classiﬁcation -- Open Source Implementation for Fall Classiﬁcation and Fall Detection Systems -- Detecting Human Activities based on a Multimodal Sensor Data Set using a Bidirectional Long Short-Term Memory Model: A Case Study -- Approaching Fall Classiﬁcation using the UP-Fall Detection Dataset: Analysis and Results from an International Competition -- Reviews and Trends on Multimodal Healthcare -- A Novel Approach for Human Fall Detection and Fall Risk Assessment.
520 $a 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.
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650 2 4 $a Biomechanics. $3 548685
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