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024 7 _a10.1007/978-3-031-60027-2
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245 1 0 _aActivity Recognition and Prediction for Smart IoT Environments
_h[electronic resource] /
_cedited by Michele Ianni, Antonella Guzzo, Raffaele Gravina, Hassan Ghasemzadeh, Zhelong Wang.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVII, 183 p. 69 illus., 58 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aInternet of Things, Technology, Communications and Computing,
_x2199-1081
505 0 _aIntroduction -- 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 _aThis 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 0 _aCooperating objects (Computer systems).
_964303
650 0 _aTelecommunication.
650 0 _aUser interfaces (Computer systems).
_964688
650 0 _aHuman-computer interaction.
_964854
650 0 _aBiometric identification.
_966861
650 1 4 _aCyber-Physical Systems.
_965638
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aUser Interfaces and Human Computer Interaction.
_966577
650 2 4 _aBiometrics.
_966862
700 1 _aIanni, Michele.
_eeditor.
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700 1 _aGuzzo, Antonella.
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700 1 _aGravina, Raffaele.
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700 1 _aGhasemzadeh, Hassan.
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_982554
700 1 _aWang, Zhelong.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783031600289
776 0 8 _iPrinted edition:
_z9783031600296
830 0 _aInternet of Things, Technology, Communications and Computing,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-60027-2
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