04342nam a22006135i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172050001600187072001600203072001700219072002300236072001500259072001600274245018700290250001800477264006900495300006500564336002600629337002600655338003600681347002400717490007700741505079700818520091801615650005102533650002302584650004702607650003902654650003702693650003502730650004202765650005902807650002302866700015102889700008603040700008703126700008903213700008303302710003403385773002603419776003603445776003603481776003603517830008403553856004603637912001403683912001403697999001703711978-3-031-60027-2DE-He21320251030112900.0cr nn 008mamaa240820s2024    sz |    s    |||| 0|eng d  a97830316002729978-3-031-60027-27 a10.1007/978-3-031-60027-22doi 4aTK7895.E42 4aTK5105.8857 7aTJF2bicssc 7aGPFC2bicssc 7aTEC0080002bisacsh 7aTJF2thema 7aGPFC2thema10aActivity Recognition and Prediction for Smart IoT Environmentsh[electronic resource] /cedited by Michele Ianni, Antonella Guzzo, Raffaele Gravina, Hassan Ghasemzadeh, Zhelong Wang.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aVII, 183 p. 69 illus., 58 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aInternet of Things, Technology, Communications and Computing,x2199-10810 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.  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. . 0aCooperating objects (Computer systems).964303 0aTelecommunication. 0aUser interfaces (Computer systems).964688 0aHuman-computer interaction.964854 0aBiometric identification.96686114aCyber-Physical Systems.96563824aCommunications Engineering, Networks.24aUser Interfaces and Human Computer Interaction.96657724aBiometrics.9668621 aIanni, Michele.eeditor.0(orcid)0000-0003-0562-74621https://orcid.org/0000-0003-0562-74624edt4http://id.loc.gov/vocabulary/relators/edt9727261 aGuzzo, Antonella.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9825521 aGravina, Raffaele.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9825531 aGhasemzadeh, Hassan.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9825541 aWang, Zhelong.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9825552 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303160026508iPrinted edition:z978303160028908iPrinted edition:z9783031600296 0aInternet of Things, Technology, Communications and Computing,x2199-108196590940uhttps://doi.org/10.1007/978-3-031-60027-2  aZDB-2-ENG  aZDB-2-SXE  c62572d62572