Human Activity Recognition and Behaviour Analysis: For Cyber-Physical Systems in Smart Environments

ISBN-13: 9783030194109 / Angielski / Miękka / 2020 / 255 str.

Liming Chen; Chris D. Nugent

Spis treści

Introduction

AR Approaches and Methods

Knowledge-Driven Approaches to AR

Hybrid Approaches to AR

Time Window-Based Data Segmentation

Semantic-Based Data Segmentation

Temporal Reasoning for Complex Activity Recognition

Fuzzy Logic for Complex Activity Recognition

Semantic Smart Home and Technology Infrastructure

Prototype Systems

O autorze

Liming Chen is Professor of Computer Science in the School of Computer Science and Informatics, De Montfort University, UK. He received his B.Eng and M.Eng from Beijing Institute of Technology, Beijing, China, and his Ph.D in Artificial Intelligence from De Montfort University, UK. His research interests include data analytics, pervasive computing, user-centered intelligent systems, smart environments and their application in smart health and care. Liming is an IET Fellow, an IEEE Senior Member, and a co-founder and co-director of the IEEE CIS “User-centred Smart Systems” task force. He has secured multi-million research funding from the UK and European Union funding bodies, industry and third countries, and acted as a coordinator and principal investigator.  Liming has over 200 peer-reviewed publications in internationally recognized journals and conferences, spanning both theoretical and applied research. He plays an active role in various scholarly activities, serving as a general or program chair for prestigious international conferences, and as associate editors and guest editor for a number of high-profile journals. He has delivered invited talks, keynotes and seminars at various forums, conferences, industry and academic events.

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.

Blurb

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.