Harnessing Artificial Intelligence and Pervasive Internet of Things for An Empowered Aging

This book provides a comprehensive overview of the latest research progress in addressing the challenges faced by the support for aging population. It covers a broad spectrum of technologies, including the Internet of Things (IoT) and Artificial Intelligence (AI), that are pivotal in supporting older people in their daily lives. The book delves into various domains such as human activity recognition, indoor localization, cognitive activity recognition, and AI-empowered health and identity management solutions. Additionally, it explores the integration of brain-computer interfaces and the development of intent-aware interactive robot-assisted systems, offering insights into how these cutting-edge technologies can be harnessed to enhance the quality of life for the older population.
This book is a collection of 15 chapters that summarize the decade-long research activities and results from the authors on tackling smart aging challenges. The book provides a detailed exposition of the latest research activities and technological breakthroughs in smart aging, and aims to bridge the gap between advanced technologies and their practical applications in enhancing the daily lives of older people. From this book, readers will be able to gain not only a wide range of topics and the corresponding technological advancements, but also a comprehensive understanding of how various technologies can be harmonized to create effective and practical smart aging solutions.

List of contents

1 Introduction.- 2 Aging Challenges and Smart Living.- 3 Technical Background.- Part I Device-free Localization and Activity Recognition.- 4 Device-Free Localization and Tracking.- 5 Device-Free Human Activity Recognition.- Part II Wearable Sensing for Activity Recognition.- 6 Wearable Sensor-based Physical Activity Recognition.- 7 EEG-based Cognitive Activity Recognition.- 8 Web-based Interactive Ambient Intelligence.- 9 Intent-aware Interactive IoT for Collaborative Ambient Intelligence.- 10 Brain-Computer Interface for IoT Cognitive Interactivity.- Part IV AI-empowered IoT Solutions.- 11 Personal Health Management.- 12 Identity Management.- 13 Large Language Models for Medical Report Generation.- 14 Emergent Capabilities of Large Foundation Models for Intelligent Navigation.- 15 Conclusion and Future Directions.

About the author

Quan Z. Sheng is a Distinguished Professor and Head of School of Computing at Macquarie University, Australia. Before moving to Macquarie University, Michael spent 10 years at School of Computer Science, the University of Adelaide, serving in a number of senior leadership roles including interim Head and Deputy Head of School of Computer Science. Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Michael worked at UNSW as a visiting research fellow. Prior to that, he spent six years as a senior software engineer in industries.
Distinguished Professor Sheng is ranked by Microsoft Academic as one of the Most Impactful Authors in Services Computing (Top 5 of All Time worldwide) and in the Web of Things (ranked Top 20 All Time) in 2021, and is ranked by ScholarGPS as one of the Highly Ranked Scholars in Web Information System (Top 5 Lifetime) in 2025. He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC (Australian Research Council) Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). Distinguished Professor Michael Sheng served as the Vice Chair of the Executive Committee of the IEEE Technical Community on Services Computing (IEEE TCSVC, 2022-2024) and has been a member of the ACS (Australian Computing Society) Technical Advisory Board on IoT since 2019.
Yao Liu is a Lecturer and Master’s Supervisor at the School of Computer Science and Engineering, Northeastern University, Shenyang, China, and an Honorary Lecturer at the School of Computing, Macquarie University, Australia. He received his Ph.D. in Computer Science and Engineering from the University of New South Wales (UNSW), Australia, and later held a Postdoctoral Research Associate position at Macquarie University in 2024. Dr. Liu’s research interests include artificial intelligence, computer vision, deep learning, and large language models, with a focus on spatio-temporal data modeling and analysis. His work spans application areas such as 3D point clouds, trajectory prediction, video understanding, and human action recognition. He has published in venues including IEEE TKDE, ACM TOMM, Knowledge-Based Systems, and ACM CIKM. He has also served as principal investigator of a sub-topic under the National Key R&D Program of China. Before his academic career, Dr. Liu worked as a software engineer at Huawei Technologies Co., Ltd.
Lina Yao is the Science Lead in Translational Machine Learning and Senior Principal Research Scientist @ CSIRO’s Data61, Conjoint Professor @ UNSW, Honorary Professor @ Macquarie University and Adjunct Professor @ University of Technology Sydney. Her research spans machine learning, data mining, information retrieval, recommender systems, and natural language processing, with a particular focus on developing robust ML systems and intelligent AI agents. She is deeply interested in explainable and personalized AI, as well as human-AI cooperation, aiming to create steerable, user-centric, and impactful systems. She is also passionate about applying AI for science and social good, leveraging cutting-edge methodologies to tackle real-world challenges and deliver meaningful societal benefits.
Professor Yao has been selected in the Clarivate Highly Cited Researcher list. She has received multiple prestigious research awards, including Australian Research Council Future Fellowship (Level 3) in 2025, Australian Research Council Discovery Early Career Research Award in 2015 and Inaugural Vice Chancellor’s Women’s Research Excellence Award from the University of Adelaide in 2015, Scientia Fellow in 2019 from the University of New South Wales, Inaugural Australia and New Zealand Women in AI Awards in 2021 and the CORE Outstanding Research Contribution Award (formerly named after Chris Wallace) in 2022.

Summary

This book provides a comprehensive overview of the latest research progress in addressing the challenges faced by the support for aging population. It covers a broad spectrum of technologies, including the Internet of Things (IoT) and Artificial Intelligence (AI), that are pivotal in supporting older people in their daily lives. The book delves into various domains such as human activity recognition, indoor localization, cognitive activity recognition, and AI-empowered health and identity management solutions. Additionally, it explores the integration of brain-computer interfaces and the development of intent-aware interactive robot-assisted systems, offering insights into how these cutting-edge technologies can be harnessed to enhance the quality of life for the older population.
This book is a collection of 15 chapters that summarize the decade-long research activities and results from the authors on tackling smart aging challenges. The book provides a detailed exposition of the latest research activities and technological breakthroughs in smart aging, and aims to bridge the gap between advanced technologies and their practical applications in enhancing the daily lives of older people. From this book, readers will be able to gain not only a wide range of topics and the corresponding technological advancements, but also a comprehensive understanding of how various technologies can be harmonized to create effective and practical smart aging solutions.

Product details

• Authors: Yao Liu, Quan Z Sheng, Quan Z. Sheng, Lina Yao
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 25.05.2026
• EAN: 9783032165015
• ISBN: 978-3-0-3216501-5
• Illustrations: Approx. 350 p.
• Subjects: Natural sciences, medicine, IT, technology > Medicine > General; Künstliche Intelligenz, Artificial Intelligence, Internet of things, Computernetzwerke und maschinelle Kommunikation, Smart Aging, Health Informatics, Artificial Intelligence (AI), Wearable sensors, Human Activity Recognition, activity recognition, Biometric Authentication, health management, Large Language Models for Medical Report Generation, Personal Health Management, Brain-Computer Interface (BCI), Intention Recognition, Health Risk Prediction, Large Foundation Models for Intelligent Navigation, Pervasive Internet of Things (IoT), Elderly Care Technology, Cognitive Monitoring, Robotics for Aging Assistance