
Intelligent Computing for Interactive System Design
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Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts.
This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities.
In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers.
This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.
More details
Content
- Intro
- Cover
- Halftitle
- Title Page
- Copyright Page
- Contents
- Preface
- Introduction
- Ubiquitous HCI
- Data-driven Opportunities and Challenges in Novel HCI
- Statistics, DSP, and ML as Tools for HCI Design and Evaluation
- Aims and Scope of this Book
- Structure of this Book
- References
- Ethical Issues in Digital Signal Processing and Machine Learning
- Ethical Frameworks
- Professional Ethics and Computer Science
- Summary
- References
- Chapter 1 Internet of Everything
- 1.1 Introduction
- 1.2 The Importance of DSP and ML in IoE Applications
- 1.3 Elements and Enabling Technologies for DSP and ML in IoE Applications
- 1.4 Computing Paradigms for IoE Data Analysis
- 1.5 Security, Privacy, and Data Confidentiality
- 1.6 Challenges and Future Directions
- 1.7 Follow-up Questions
- References
- Chapter 1E The Internet of Everything-Introducing Privacy
- References
- Chapter 2 Statistical Grounding
- 2.1 Terminologies
- 2.2 Sample and Population
- 2.3 Level of Measurement
- 2.4 Data Collection and Logging
- 2.5 Descriptive Statistics
- 2.6 Outliers
- 2.7 Hypothesis Testing
- 2.8 Parametric Tests
- 2.9 Non-parametric Tests
- 2.10 Case Study
- 2.11 Further Reading
- References
- Chapter 2E Ethics and Statistics
- References
- Chapter 3 DSP Basics
- 3.1 Introduction
- 3.2 Signals
- 3.3 Analog-to-digital Conversion
- 3.4 Digital-to-analog Conversion
- 3.5 Discrete Fourier Transform
- 3.6 Autocorrelation
- 3.7 LTI Systems
- 3.8 Use Case Example
- 3.9 Follow-up Questions
- 3.10 Summary
- References
- Chapter 3E Ethical Issues of Digital Signal Processing
- References
- Chapter 4 Machine Learning Basics
- 4.1 Probability Primer
- 4.2 Supervised Learning
- 4.3 Unsupervised Learning: Clustering
- 4.4 Practical Aspects
- 4.5 Summary and Links
- 4.6 Follow-up Questions
- References
- Chapter 4E Ethical Issues in Machine Learning
- References
- Chapter 5 Combining Infrastructure Sensor and Tourism Market Data in a Smart City Project-Case Study 1
- 5.1 Tourism Analytics
- 5.2 Making Sense of the Available Glasgow Data
- 5.3 Predicting Business Indicators
- 5.4 Multivariate Predictions
- 5.5 Simple Visualization
- 5.6 Summary
- Acknowledgments
- Author Contributions
- References
- Chapter 5E Ethics and Smart Cities
- References
- Chapter 6 Brain-Computer Interfacing with Interactive Systems-Case Study 2
- 6.1 Introduction
- 6.2 Interfacing with the Brain
- 6.3 Interacting with VR
- 6.4 Conclusions
- 6.5 Follow-up Questions
- 6.6 Further Reading
- Acknowledgments
- References
- Chapter 6E Ethical Issues in Brain-Computer Interfaces
- References
- Chapter 7 Probabilistic Text Entry-Case Study 3
- 7.1 Uncertain Text Input
- 7.2 Statistical Formulation
- 7.3 Input Modeling
- 7.4 Language Modeling
- 7.5 Decoding
- 7.6 User Interface Issues
- 7.7 Case Study: Typing on a Smartwatch
- 7.8 Discussion and Conclusions
- 7.9 Follow-up Questions
- 7.10 Further Reading
- Acknowledgments
- References
- Chapter 7E Ethical Issues in Probabilistic Text Entry
- References
- Chapter 8 Secure Gestures-Case Study 4
- 8.1 What Are Secure Gestures?
- 8.2 Background: The Problem of Recognition
- 8.3 Recognition Approaches
- 8.4 Metrics for Evaluating Recognition Approaches
- 8.5 Attacks Against Gesture-based Authentication
- 8.6 Summary
- 8.7 Follow-up Question
- 8.8 Further Reading
- Acknowledgments
- References
- Chapter 8E Ethics and Secure Gestures
- References
- Chapter 9 Personal Context from Mobile Phones-Case Study 5
- 9.1 What Is Personal Context?
- 9.2 Example: Inferring Phone Placement
- 9.3 Inferring Social Relationships from Communication Behavior
- 9.4 Conclusion
- 9.5 Follow-up Questions
- Acknowledgments
- References
- Chapter 9E Ethics and Personal Context
- References
- Chapter 10 Building Adaptive Touch Interfaces-Case Study 6
- 10.1 Motivation for Adaptive, Probabilistic Touch Interfaces
- 10.2 Three Key Challenges for Developing Adaptive Touch Interfaces
- 10.3 The ProbUI Framework
- 10.4 Development Examples
- 10.5 Reflection from a Developer Perspective
- 10.6 Conclusion and Outlook
- 10.7 Follow-up Questions
- 10.8 Further Reading
- Acknowledgments
- References
- Chapter 10E Ethics and Adaptive Touch Interfaces
- References
- Chapter 11 Driver Cognitive Load Classification Based on Physiological Data-Case Study 7
- 11.1 Motivation for Driver State Estimation
- 11.2 Driver Cognitive Load Detection Methods
- 11.3 Case Study: Cognitive Load Classification Using Driver Physiological Data
- 11.4 Conclusion
- 11.5 Further Reading
- 11.6 Follow-up Questions
- Acknowledgments
- References
- Chapter 11E Ethics in Automotive User Interface
- References
- Authors' Biographies
- Index
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