Artificial Intelligence for Human Computer Interaction: A Modern Approach

  • 13h 9m
  • Otmar Hilliges, Yang Liu
  • Springer
  • 2022

This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address.

It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.

In this Book

  • Forward for Artificial Intelligence for Human Computer Interaction—A Modern Approach
  • Introduction
  • Human Performance Modeling with Deep Learning
  • Optimal Control to Support High-Level User Goals in Human-Computer Interaction
  • Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning
  • Eye Gaze Estimation and Its Applications
  • AI-Driven Intelligent Text Correction Techniques for Mobile Text Entry
  • Deep Touch—Sensing Press Gestures from Touch Image Sequences
  • Deep Learning-Based Hand Posture Recognition for Pen Interaction Enhancement
  • An Early Rico Retrospective—Three Years of Uses for a Mobile App Dataset
  • Visual Intelligence through Human Interaction
  • ML Tools for the Web—A Way for Rapid Prototyping and HCI Research
  • Interactive Reinforcement Learning for Autonomous Behavior Design
  • Sketch-Based Creativity Support Tools Using Deep Learning
  • Generative Ink—Data-Driven Computational Models for Digital Ink
  • Bridging Natural Language and Graphical User Interfaces
  • Demonstration + Natural Language—Multimodal Interfaces for GUI-Based Interactive Task Learning Agents
  • Human-Centered AI for Medical Imaging
  • 3D Spatial Sound Individualization with Perceptual Feedback
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