國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Challenges and trends in multimodal ...

Ponce, Hiram.

Challenges and trends in multimodal fall detection for healthcare

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Challenges and trends in multimodal fall detection for healthcareedited by Hiram Ponce ... [et al.].
其他作者:	Ponce, Hiram.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xiii, 259 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Biosensors.
電子資源:	https://doi.org/10.1007/978-3-030-38748-8
ISBN:	9783030387488$q(electronic bk.)

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.3..

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$q(electronic bk.)

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

188824
Biosensors.

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

Dewey Class. No.: 610.285

Challenges and trends in multimodal fall detection for healthcare
LDR:02583nmm a2200337 a 4500 001 573550
003 DE-He213
005 20200620143834.0
006 m d
007 cr nn 008maaau
008 200928s2020 sz s 0 eng d
020 $a 9783030387488$q(electronic bk.)
020 $a 9783030387471$q(paper)
024 7 $a 10.1007/978-3-030-38748-8 $2 doi
035 $a 978-3-030-38748-8
040 $a GP $c GP
041 0 $a eng
050 4 $a R857.B54 $b C435 2020
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.285 $2 23
090 $a R857.B54 $b C437 2020
245 0 0 $a Challenges and trends in multimodal fall detection for healthcare $h [electronic resource] / $c edited by Hiram Ponce ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 259 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Biosensors. $3 188824
650 0 $a Falls (Accidents) in old age. $3 753724
650 1 4 $a Biomedical Engineering and Bioengineering. $3 826326
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Biomechanics. $3 188942
700 1 $a Ponce, Hiram. $3 860883
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in systems, decision and control ; $v v.3. $3 678532
856 4 0 $u https://doi.org/10.1007/978-3-030-38748-8
950 $a Engineering (Springer-11647)