國立虎尾科技大學 |

Challenges and Trends in Multimodal Fall Detection for Healthcare

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Challenges and Trends in Multimodal Fall Detection for Healthcare/ edited by Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor.
其他作者:	Moya-Albor, Ernesto.
面頁冊數:	XIII, 259 p.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational Intelligence. -
電子資源:	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, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor. - 1st ed. 2020. - XIII, 259 p.online resource. - Studies in Systems, Decision and Control,2732198-4182 ;. - Studies in Systems, Decision and Control,27.

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:

768837
Computational Intelligence.

LC Class. No.: R856-857

Dewey Class. No.: 610.28

Challenges and Trends in Multimodal Fall Detection for Healthcare
LDR:03023nam a22004335i 4500 001 1019081
003 DE-He213
005 20200701022450.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030387488 $9 978-3-030-38748-8
024 7 $a 10.1007/978-3-030-38748-8 $2 doi
035 $a 978-3-030-38748-8
050 4 $a R856-857
050 4 $a HC79.E5
050 4 $a GE220
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
245 1 0 $a Challenges and Trends in Multimodal Fall Detection for Healthcare $h [electronic resource] / $c edited by Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva, Ernesto Moya-Albor.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIII, 259 p. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Systems, Decision and Control, $x 2198-4182 ; $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.
650 2 4 $a Computational Intelligence. $3 768837
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 0 $a Biomechanics. $3 565307
650 0 $a Computational intelligence. $3 568984
650 0 $a Biomedical engineering. $3 588770
700 1 $a Moya-Albor, Ernesto. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1314229
700 1 $a Brieva, Jorge. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1309257
700 1 $a Martínez-Villaseñor, Lourdes. $e editor. $1 https://orcid.org/0000-0002-9038-7821 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1299574
700 1 $a Ponce, Hiram. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1314228
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030387471
776 0 8 $i Printed edition: $z 9783030387495
776 0 8 $i Printed edition: $z 9783030387501
830 0 $a Studies in Systems, Decision and Control, $x 2198-4182 ; $v 27 $3 1254124
856 4 0 $u https://doi.org/10.1007/978-3-030-38748-8
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)