Papier-Mache: Toolkit Support for Tangible Input HCI Group University of California Scott R. Klemmer Jack Li James Lin DUB Group University of Washington James A. Landay CHI 2004

Outline Introduction Inspiring Tangible Interfaces Structured Interview with TUI Designers The Papier-Mache Architecture Evaluation Conclusion 2

Introduction 3

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Papier Mache No input/graphic hardware experts Several types of physical input A monitoring window (Wizard of Oz) UI = API 7

8 Spatial Topological Associative Forms

TUI – Spatial Application 9

TUI –Topological Application 10

TUI – Associative Application 11

TUI – Forms Application 12

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TUI - Functionality Physical input for arranging electronic content Physical input for invoking action Electronic capture of physical structures Coordinating physical input and graphical output An add, update, remove event structure 14

Interview 9 Researchers TUI experienced Vision, RF & capacitance sensors, and barcode experienced Via , phone, face to face 15

Interview - Feedback No small matter of programming – Acquiring and abstracting input The appropriate abstraction is events, not widgets – Model-View-Controller (Event-based) – View-Controller combination  widget 16

Interview - Feedback Authoring behavior: associations and classifications – Couple physical input with electronic behavior Importance of feedback for users and developers – Visual feedback about system’s perception 17

The Papier-Mache Architecture Simultaneous input objects Application portability Uniform events Classifying and association input Visual Feedback 18

The Papier-Mache Architecture JDK + JMF + JAI Computer vision RFID Barcode 19

The Papier-Mache Architecture Input abstraction & event generation Association & classification Program monitoring: application state display Lowering the threshold: a simple application 20

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Evaluation Software engineering view – Performance – Reliability – Lines of code HCI view – Ease of use – Facilitating reuse – Schemas yield similar code 23

Use Case in Coursework Easy, useful and clear Code reducing Recogintion accuracy 24

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Use Case in Lab User: 1 in graphics, 3 in programming, 2 in systems and 1 in AI (all java experienced) Programming task: computer vision & RFID Result – Task1: 31min & 19 lines of code – Task2: 33min & 38 lines of code – Without learning Hardware connection Recognition Event generation 26

Conclusion & Future Work Event-based model  portability WOz  association & classification Optimize the vision system Demand lower latency 27

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