國立虎尾科技大學 |

Activity recognition and prediction for smart IoT environments

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Activity recognition and prediction for smart IoT environments/ edited by Michele Ianni ...[et al.].
其他作者:	Ianni, Michele.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	vii, 183 p. :ill. (chiefly col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Biometrics. -
電子資源:	https://doi.org/10.1007/978-3-031-60027-2
ISBN:	9783031600272

Activity recognition and prediction for smart IoT environments
Activity recognition and prediction for smart IoT environments[electronic resource] /edited by Michele Ianni ...[et al.]. - Cham :Springer Nature Switzerland :2024. - vii, 183 p. :ill. (chiefly col.), digital ;24 cm. - Internet of things, technology, communications and computing,2199-1081. - Internet of things, technology, communications and computing..

Introduction -- Methodology for human activity recognition based on wearable sensor networks -- Efficient Sensing and Classification for Extended Battery Life -- Multi-user activity monitoring based on contactless sensing -- An efficient approach exploiting Ensemble Learning for Human Activity Recognition -- Activity Recognition Using 2-D LiDAR based on Improved MobileNet -- Habit mining through process-mining techniques. Survey and research challenges -- The role of ML in Activity Recognition in the Industry 4.0 -- IoT Based HAR patterns using Sensors based Approach in smart environment and enabled assistive technologies -- Trace2AR: a novel embedding for the detection of complex activity recognition -- Situation Aware Wearable Systems for Human Activity Recognition -- Conclusion.

This book provides the latest developments in activity recognition and prediction, with particular focus on the Internet of Things. The book covers advanced research and state of the art of activity prediction and its practical application in different IoT related contexts, ranging from industrial to scientific, from business to daily living, from education to government and so on. New algorithms, architectures, and methodologies are proposed, as well as solutions to existing challenges with a focus on security, privacy, and safety. The book is relevant to researchers, academics, professionals and students. Provides a comprehensive review of the field of activity recognition; Covers an array of topics and applications illustrating the use of activity recognition in IoT related scenarios; Explains how to extract value from application logs and use the data to classify activities and predict actions.

ISBN: 9783031600272

Standard No.: 10.1007/978-3-031-60027-2doiSubjects--Topical Terms:

677095
Biometrics.

LC Class. No.: TK5105.8857

Dewey Class. No.: 004.678

Activity recognition and prediction for smart IoT environments
LDR:02892nam a22003735a 4500 001 1134997
003 DE-He213
005 20240820130235.0
006 m d
007 cr nn 008maaau
008 241213s2024 sz s 0 eng d
020 $a 9783031600272 $q (electronic bk.)
020 $a 9783031600265 $q (paper)
024 7 $a 10.1007/978-3-031-60027-2 $2 doi
035 $a 978-3-031-60027-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.8857
072 7 $a TJF $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a GPFC $2 thema
082 0 4 $a 004.678 $2 23
090 $a TK5105.8857 $b .A188 2024
245 0 0 $a Activity recognition and prediction for smart IoT environments $h [electronic resource] / $c edited by Michele Ianni ...[et al.].
260 $a Cham : $c 2024. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a vii, 183 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a Internet of things, technology, communications and computing, $x 2199-1081
505 0 $a Introduction -- Methodology for human activity recognition based on wearable sensor networks -- Efficient Sensing and Classification for Extended Battery Life -- Multi-user activity monitoring based on contactless sensing -- An efficient approach exploiting Ensemble Learning for Human Activity Recognition -- Activity Recognition Using 2-D LiDAR based on Improved MobileNet -- Habit mining through process-mining techniques. Survey and research challenges -- The role of ML in Activity Recognition in the Industry 4.0 -- IoT Based HAR patterns using Sensors based Approach in smart environment and enabled assistive technologies -- Trace2AR: a novel embedding for the detection of complex activity recognition -- Situation Aware Wearable Systems for Human Activity Recognition -- Conclusion.
520 $a This book provides the latest developments in activity recognition and prediction, with particular focus on the Internet of Things. The book covers advanced research and state of the art of activity prediction and its practical application in different IoT related contexts, ranging from industrial to scientific, from business to daily living, from education to government and so on. New algorithms, architectures, and methodologies are proposed, as well as solutions to existing challenges with a focus on security, privacy, and safety. The book is relevant to researchers, academics, professionals and students. Provides a comprehensive review of the field of activity recognition; Covers an array of topics and applications illustrating the use of activity recognition in IoT related scenarios; Explains how to extract value from application logs and use the data to classify activities and predict actions.
650 2 4 $a Biometrics. $3 677095
650 2 4 $a User Interfaces and Human Computer Interaction. $3 669793
650 2 4 $a Communications Engineering, Networks. $3 669809
650 1 4 $a Cyber-Physical Systems. $3 1387591
650 0 $a Activity trackers (Wearable technology) $3 1436056
650 0 $a Internet of things. $3 1023130
700 1 $a Ianni, Michele. $3 1456586
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Internet of things, technology, communications and computing. $3 1104780
856 4 0 $u https://doi.org/10.1007/978-3-031-60027-2
950 $a Engineering (SpringerNature-11647)