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μV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture Recognizer by Nathan Magrofuoco; Paolo Roselli; Jean Vanderdonckt is a Computer Science article available to read on EtoBox.

What is μV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture Recognizer about?

Finger-based gesture input becomes a major interaction modality for surface computing. Due to the low precision of the finger and the variation in gesture production, multistroke gestures are still challenging to recognize in various setups. In this paper, we present μV, a multistroke gesture recognizer that addresses the properties of articulation, rotation, scaling, and translation invariance by combining $P+'s cloud-matching for articulation invariance with !FTL's local shape distance for RST-invariance. We evaluate μV against five competitive recognizers on MMG, an existing gesture set, and on two new versions for smartphones and tablets, MMG+ and RMMG+, a randomly rotated version on both platforms. μV is significantly more accurate than its predecessors when rotation invariance is required and not significantly inferior when it is not. μV is also significantly faster than others with many samples and not significantly slower with few samples.

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Author

Nathan Magrofuoco; Paolo Roselli; Jean Vanderdonckt

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)