04528nam a22006615i 4500 978-3-031-60027-2 DE-He213 20251030112900.0 cr nn 008mamaa 240820s2024 sz | s |||| 0|eng d 9783031600272 978-3-031-60027-2 10.1007/978-3-031-60027-2 doi TK7895.E42 TK5105.8857 TJF bicssc GPFC bicssc TEC008000 bisacsh TJF thema GPFC thema Activity Recognition and Prediction for Smart IoT Environments [electronic resource] / edited by Michele Ianni, Antonella Guzzo, Raffaele Gravina, Hassan Ghasemzadeh, Zhelong Wang. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VII, 183 p. 69 illus., 58 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Internet of Things, Technology, Communications and Computing, 2199-1081 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. . Cooperating objects (Computer systems). 64303 Telecommunication. User interfaces (Computer systems). 64688 Human-computer interaction. 64854 Biometric identification. 66861 Cyber-Physical Systems. 65638 Communications Engineering, Networks. User Interfaces and Human Computer Interaction. 66577 Biometrics. 66862 Ianni, Michele. editor. (orcid)0000-0003-0562-7462 https://orcid.org/0000-0003-0562-7462 edt http://id.loc.gov/vocabulary/relators/edt 72726 Guzzo, Antonella. editor. edt http://id.loc.gov/vocabulary/relators/edt 82552 Gravina, Raffaele. editor. edt http://id.loc.gov/vocabulary/relators/edt 82553 Ghasemzadeh, Hassan. editor. edt http://id.loc.gov/vocabulary/relators/edt 82554 Wang, Zhelong. editor. edt http://id.loc.gov/vocabulary/relators/edt 82555 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031600265 Printed edition: 9783031600289 Printed edition: 9783031600296 Internet of Things, Technology, Communications and Computing, 2199-1081 65909 https://doi.org/10.1007/978-3-031-60027-2 ZDB-2-ENG ZDB-2-SXE 62572 62572