µV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture Recognizer-Reference-Cited by-同舟云学术

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

Published:2022-06-14 Issue:EICS Volume:6 Page:1-25
ISSN:2573-0142
Container-title:Proceedings of the ACM on Human-Computer Interaction
language:en
Short-container-title:Proc. ACM Hum.-Comput. Interact.

Author:

Magrofuoco Nathan¹,Roselli Paolo²,Vanderdonckt Jean¹

Affiliation:

1. Université catholique de Louvain, Louvain-la-Neuve, Belgium

2. Università degli Studi di Roma "Tor Vergata", Rome, Italy

Abstract

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.

Funder

FRIA

UCLouvain Fonds Spéciaux de Recherche

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

Link

https://dl.acm.org/doi/pdf/10.1145/3532200

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