Authors:
- Provides a comprehensive and systematic introduction to activity recognition
- Presents an in-depth discussion on the knowledge-driven approach to activity recognition
- Proposes a systematic methodology, along with a scalable framework and extensible technology infrastructure
- Offers a sample technology solution for smart healthcare
- Reviews the latest, state-of-the-art research, covering aspects of both methodology and application
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
The 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.
Authors and Affiliations
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School of Computer Science and Informatics, De Montfort University, Leicester, UK
Liming Chen
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School of Computing, Ulster University, Belfast, UK
Chris D. Nugent
About the authors
Chris D. Nugent is currently the Head of the School of Computing at Ulster University and leads the Pervasive Computing Research Group. He received a Bachelor of Engineering in Electronic Systems and DPhil in Biomedical Engineering both from Ulster University and currently holds the position of Professor of Biomedical Engineering.
His research within biomedical engineering addresses the themes of the development and evaluation of technologies to support pervasive healthcare within smart environments. Specifically, this has involved research in the topics of mobile based reminding solutions, activity recognition and behaviour modelling and more recently technology adoption modelling. He has published over 450 papers in these areas and has been a grant holder of Research Projects funded by National, European and International funding bodies.
He is the co-Principal Investigator of the Connected Health Innovation Centre at Ulster University and a co-Investigator of the British Telecom Ireland Innovation Centre. In 2016 he was awarded the Senior Distinguished Research Fellowship from Ulster University.
Bibliographic Information
Book Title: Human Activity Recognition and Behaviour Analysis
Book Subtitle: For Cyber-Physical Systems in Smart Environments
Authors: Liming Chen, Chris D. Nugent
DOI: https://doi.org/10.1007/978-3-030-19408-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-19407-9Published: 19 June 2019
Softcover ISBN: 978-3-030-19410-9Published: 15 August 2020
eBook ISBN: 978-3-030-19408-6Published: 11 June 2019
Edition Number: 1
Number of Pages: XXIV, 255
Number of Illustrations: 16 b/w illustrations, 65 illustrations in colour
Topics: Pattern Recognition, Computer Appl. in Social and Behavioral Sciences, Information Systems Applications (incl. Internet), Computer Communication Networks, Big Data/Analytics, Input/Output and Data Communications
Industry Sectors: Aerospace, Biotechnology, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Law, Pharma, Telecommunications