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Activity recognition and prediction for smart IoT environments

Record Type:	Electronic resources : Monograph/item
Title/Author:	Activity recognition and prediction for smart IoT environments/ edited by Michele Ianni ...[et al.].
other author:	Ianni, Michele.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	vii, 183 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Internet of things. -
Online resource:	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:

2057703
Internet of things.

LC Class. No.: TK5105.8857

Dewey Class. No.: 004.678

Activity recognition and prediction for smart IoT environments
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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 : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a vii, 183 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
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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.
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650 2 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a User Interfaces and Human Computer Interaction. $3 892554
650 2 4 $a Biometrics. $3 898232
700 1 $a Ianni, Michele. $3 3753166
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