TY  - BOOK
AU  - Ianni,Michele
AU  - Guzzo,Antonella
AU  - Gravina,Raffaele
AU  - Ghasemzadeh,Hassan
AU  - Wang,Zhelong
ED  - SpringerLink (Online service)
TI  - Activity Recognition and Prediction for Smart IoT Environments
T2  - Internet of Things, Technology, Communications and Computing,
SN  - 9783031600272
AV  - TK7895.E42 
U1  - 621.38 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Cooperating objects (Computer systems)
KW  - Telecommunication
KW  - User interfaces (Computer systems)
KW  - Human-computer interaction
KW  - Biometric identification
KW  - Cyber-Physical Systems
KW  - Communications Engineering, Networks
KW  - User Interfaces and Human Computer Interaction
KW  - Biometrics
N1  - 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
N2  - 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.
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-60027-2
ER  -