Tilt-Responsive Techniques for Digital Drawing Boards


Hugo Romat, Christopher Collins, Nathalie Riche, Michel Pahud, Christian Holz, Adam Riddle, Bill Buxton, Ken Hinckley


Drawing boards offer a self-stable work surface that is continuously adjustable. On digital displays, such as the Microsoft Surface Studio, these properties open up a class of techniques that sense and respond to tilt adjustments. Each display posture—whether angled high, low, or somewhere in-between—affords some activities, but not others. Because
what is appropriate also depends on the application and task, we explore a range of app-specific transitions between reading vs. writing (annotation), public vs. personal, shared person-space vs. task-space, and other nuances of input and feedback, contingent on display angle. Continuous responses provide interactive transitions tailored to each use-case. We show how a variety of knowledge work scenarios can use sensed display adjustments to drive context-appropriate transitions, as well as technical software details of how to best realize these concepts. A preliminary remote user study suggests that techniques must balance effort required to adjust tilt, versus the potential benefits of a sensed transition.


  • [IMG]
    H. Romat, C. Collins, N. Riche, M. Pahud, C. Holz, A. Riddle, B. Buxton, and K. Hinckley, “Tilt-Responsive Techniques for Digital Drawing Boards,” in UIST ’20: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, 2020.
    [Bibtex] [PDF]

      author = {Hugo Romat and Christopher Collins and Nathalie Riche and Michel Pahud and Christian Holz and Adam Riddle and Bill Buxton and Ken Hinckley},
      title = {Tilt-Responsive Techniques for Digital Drawing Boards},
      booktitle = {UIST '20: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology},
      month = {October},
      year = {2020},
      publisher = {ACM},



Érudit and Vialab Collaboration Projects

Academia is Tied in Knots

Tilt-Responsive Techniques for Digital Drawing Boards

Textension: Digitally Augmenting Document Spaces in Analog Texts

Eye Tracking for Target Acquisition in Sparse Visualizations

Guidance in the human–machine analytics process

H-Matrix: Hierarchical Matrix for Visual Analysis of Cross-Linguistic Features in Large Learner Corpora

A Visual Analytics Framework for Adversarial Text Generation

Design by Immersion: A Transdisciplinary Approach to Problem-Driven Visualizations

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

Discriminability Tests for Visualization Effectiveness and Scalability

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

ActiveInk: (Th)Inking with Data

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

Perceptual Biases in Font Size as a Data Encoding

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

PhysioEx: Visual Analysis of Physiological Event Streams

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Off-Screen Desktop


Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Balancing Clutter and Information in Large Hierarchical Visualizations

Lexichrome: Text Construction and Lexical Discovery with Word-Color Associations Using Interactive Visualization

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization




Simple Multi-Touch Toolkit

Exploring Text Entities with Descriptive Non-photorealistic Rendering

Investigating the Semantic Patterns of Passwords

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations

Parallel Tag Clouds to Explore Faceted Text Corpora

VisLink: Revealing Relationships Amongst Visualizations

DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

// Where the sidebar information is stored
| © Copyright vialab | Dr. Christopher Collins, Canada Research Chair in Linguistic Information Visualization |