000 04535nam a22006135i 4500
001 978-3-030-19408-6
003 DE-He213
005 20240423125213.0
007 cr nn 008mamaa
008 190611s2019 sz | s |||| 0|eng d
020 _a9783030194086
_9978-3-030-19408-6
024 7 _a10.1007/978-3-030-19408-6
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQP
_2thema
082 0 4 _a006.4
_223
100 1 _aChen, Liming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aHuman Activity Recognition and Behaviour Analysis
_h[electronic resource] :
_bFor Cyber-Physical Systems in Smart Environments /
_cby Liming Chen, Chris D. Nugent.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXXIV, 255 p. 81 illus., 65 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1. Introduction -- Chapter 2. Sensor-based Activity Recognition Review -- Chapter 3. An Ontology-based Approach to Activity Recognition -- Chapter 4. A Hybrid Approach to Activity Modelling -- Chapter 5. Time-window based Data Segmentation -- Chapter 6. Semantic-based Sensor Data Segmentation -- Chapter 7. Composite Activity Recognition -- Chapter 8. Semantic Smart Homes: Towards a Knowledge-rich Smart Environment -- Chapter 9. Semantic Smart Homes: Situation-aware Assisted Living -- Chapter 10. Human Centred Cyber Physical Systems.
520 _aThe book first defines the problems, various concepts and notions related to activity recognition, and introduces the fundamental rationale and state-of-the-art methodologies and approaches. It then describes the use of artificial intelligence techniques and advanced knowledge technologies for the modelling and lifecycle analysis of human activities and behaviours based on real-time sensing observations from sensor networks and the Internet of Things. It also covers inference and decision-support methods and mechanisms, as well as personalization and adaptation techniques, which are required for emerging smart human-machine pervasive systems, such as self-management and assistive technologies in smart healthcare. Each chapter includes theoretical background, technological underpinnings and practical implementation, and step-by-step information on how to address and solve specific problems in topical areas. This monograph can be used as a textbook for postgraduate and PhD students on courses such as computer systems, pervasive computing, data analytics and digital health. It is also a valuable research reference resource for postdoctoral candidates and academics in relevant research and application domains, such as data analytics, smart cities, smart energy, and smart healthcare, to name but a few. Moreover, it offers smart technology and application developers practical insights into the use of activity recognition and behaviour analysis in state-of-the-art cyber-physical systems. Lastly, it provides healthcare solution developers and providers with information about the opportunities and possible innovative solutions for personalized healthcare and stratified medicine.
650 0 _aPattern recognition systems.
650 0 _aSocial sciences
_xData processing.
650 0 _aApplication software.
650 0 _aComputer networks .
650 0 _aQuantitative research.
650 0 _aComputer input-output equipment.
650 1 4 _aAutomated Pattern Recognition.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aComputer Communication Networks.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aInput/Output and Data Communications.
700 1 _aNugent, Chris D.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030194079
776 0 8 _iPrinted edition:
_z9783030194093
776 0 8 _iPrinted edition:
_z9783030194109
856 4 0 _uhttps://doi.org/10.1007/978-3-030-19408-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175386
_d175386