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2019 | Buch

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Human Activity Recognition and Behaviour Analysis

For Cyber-Physical Systems in Smart Environments

verfasst von: Prof. Liming Chen, Prof. Chris D. Nugent

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

This chapter provides an overview on the background, basic concepts, existing approaches and methodologies, potential applications, opportunities and research trends and directions for computational behaviour analysis. It first introduces the background and context of this book, and the basic concepts and terms used in the discussion of activity recognition in the book. It then provides a high-level review on dominant approaches and methods that have been used for activity recognition in related research communities. Following this, it discusses potential application domains and particularly highlights the role and opportunities of activity recognition in ambient assisted living, which has recently been under vigorous investigation, and also serves as the main application scenario in our discussions throughout the book. Finally the chapter presents research trends and directions of this research field. This chapter is intended to provide necessary technical background and context for readers to help them best prepared for reading and understanding the book.

Liming Chen, Chris D. Nugent

Chapter 2. Sensor-Based Activity Recognition Review

Abstract

This chapter presents a comprehensive survey on the state of the art of various aspects of sensor-based activity recognition. It first examines the general rationale and distinctions of different sensor technologies for activity monitoring. Then we review the major approaches and methods associated with sensor-based activity modeling and recognition from which strengths and weaknesses of those approaches are analysed and highlighted. The survey makes a primary distinction between data-driven and knowledge-driven approaches, and uses this distinction to structure our survey.

Liming Chen, Chris D. Nugent

Chapter 3. An Ontology-Based Approach to Activity Recognition

Abstract

This chapter introduces an ontology-based knowledge-driven approach to real-time, continuous activity recognition based on multi-sensor data streams in the context of assisted living within smart homes. It first presents a generic system architecture for the proposed knowledge-driven approach and its underlying ontology-based activity recognition process. It then analyses the characteristics of smart homes and Activities of Daily Living (ADL) upon which both context and ADL ontologies are developed. Following this, the chapter describes algorithms for activity recognition based on semantic subsumption reasoning. Finally, an example case study is conducted using an implemented function-rich software system, which evaluates and demonstrates the proposed approach through extensive experiments involving a number of various ADL use scenarios.

Liming Chen, Chris D. Nugent

Chapter 4. A Hybrid Approach to Activity Modelling

Abstract

This chapter introduces an ontology-based hybrid approach to activity modeling that combines domain knowledge-based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are then put in use, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this chapter focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. An example case study has been used to demonstrate and evaluate the activity learning algorithms and mechanisms through which well-designed experiments in a feature-rich assistive living system.

Liming Chen, Chris D. Nugent

Chapter 5. Time-Window Based Data Segmentation

Abstract

This chapter presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The chapter first analyses the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of ontology-based activity recognition. An example case study has been undertaken in a number of experiments to evaluate and demonstrate the developed approach in a prototype system.

Liming Chen, Chris D. Nugent

Chapter 6. Semantic-Based Sensor Data Segmentation

Abstract

This chapter introduces an approach to semantically distinguishing individual sensor events directly to relevant constituent activities in the context of interleaved and concurrent activity recognition. It first reviews related work and highlights the needs and challenges of data segmentation of composite activity recognition. It then proposes a semiotic theory inspired ontological model, capturing generic knowledge and inhabitant-specific preferences for conducting ADLs to support the segmentation process. Following this, the chapter presents a multithread semantic based segmentation algorithm for dynamic sensor segmentation of composite activities. Finally, the chapter describes an example case study to evaluate and demonstrate the proposed approach in an implemented system prototype.

Liming Chen, Chris D. Nugent

Chapter 7. Composite Activity Recognition

Abstract

Activity recognition is essential in providing activity assistance for users in smart homes. While significant progress has been made for single-user single-activity recognition, it still remains a challenge to carry out real-time progressive composite activity recognition. This Chapter introduces a hybrid approach to composite activity modelling and recognition by extending existing ontology-based knowledge-driven approach with temporal modelling and reasoning methods. It combines and describes in details ontological activity modelling which establishes relationships between activities and their involved entities, and temporal activity modelling which defines relationships between constituent activities of a composite activity, thus providing powerful representation capabilities for composite activity modelling. The Chapter describes an integrated architecture for composite activity recognition, and elaborates a unified activity recognition algorithm for the recognition of simple and composite activities. As an essential part of the model, the Chapter also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. An example case study has been undertaken using a number of experiments to evaluate and demonstrate the proposed approach in a feature-rich multi-agent prototype system.

Liming Chen, Chris D. Nugent

Chapter 8. Semantic Smart Homes: Towards a Knowledge-Rich Smart Environment

Abstract

This chapter introduces semantic smart homes—a novel concept whose aim is to move from the current state of the art of smart home technologies to the future infrastructure that is needed to support the full richness of the smart home vision in which there are adaptive, personalised and context-aware assistance capabilities. It describes the rationale behind the conception and presents a conceptual system architecture for semantic smart homes. It then elaborates functions and their interplay of constituent components with specific emphasis being placed on the methodology of semantic modeling, content generation and management. The chapter also discusses the semantic-enabled processing capabilities and the potentials of the semantic smart homes metaphor through a number of use scenarios.

Liming Chen, Chris D. Nugent

Chapter 9. Semantic Smart Homes: Situation-Aware Assisted Living

Abstract

This chapter introduces a systematic approach to providing situation-aware ADL assistances using the semantic smart home environment. It first analyses the nature and issues of SH-based healthcare for cognitively deficient inhabitants, and discusses the ways in which semantic technologies enhance situation comprehension. It then presents an intelligent agent system for interpreting and reasoning semantic situational (meta)data to enhance situation-aware decision support. The Chapter drills down to provide details of semantic sensor data modelling, fusion and storage and retrieval mechanisms, which creates machine understandable and processable situational data. It also describes a cognitive agent for realizing high-level cognitive capabilities such as prediction and explanation by exploiting the generated semantic content. An example case study has been conducted using a prototype agent-based assistive system to illustrate the proposed approach through simulated and real-time ADL assistance scenarios in the context of situation aware assistive living.

Liming Chen, Chris D. Nugent

Chapter 10. Human Centred Cyber Physical Systems

Abstract

This Chapter presents four prototype systems which have been developed for testing and evaluating various activity recognition approaches investigated in previous chapters. These prototype systems are categorised based on the styles of their software architecture into a standalone, a multi-agent and two SOA systems. This reflects and closely corresponds to the evolution of the latest technologies in software engineering and smart cyber-physical systems. Given that previous chapters have already described how systems are used for specific use scenarios, this chapter has focused on the implementation details and operation processes of these systems which are described one by one in four sections. It is expected that interested researchers can use these systems or follow the implementation methodologies to support their research. In addition, the performance, strengths and limitations, and future work of these systems are also discussed.

Liming Chen, Chris D. Nugent

Backmatter

Titel: Human Activity Recognition and Behaviour Analysis
verfasst von: Prof. Liming Chen
Prof. Chris D. Nugent
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-19408-6
Print ISBN: 978-3-030-19407-9
DOI: https://doi.org/10.1007/978-3-030-19408-6