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Differences and Similarities between Finger and Pen Stroke Gestures on Stationary and Mobile devices

Authors:

Huawei Tu,

Xiangshi Ren,

Shumin ZhaiAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 22, Issue 5

Article No.: 22, Pages 1 - 39

https://doi.org/10.1145/2797138

Published: 10 August 2015 Publication History

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Abstract

This study investigated differences and similarities between finger and pen gestures on stationary devices (sitting posture) and mobile devices (sitting and walking postures). The recorded gestures were analyzed according to multiple gesture features. We found (1) pen and index finger gestures were different in features like size ratio but similar in features like angle difference; (2) implement (pen vs. index finger vs. thumb) interacted with gesture complexity and size in features like articulation time; (3) features like time and shape distance, were different between the pen and index finger on mobile devices (walking) but similar on stationary devices; (4) one-handed thumb gestures had worse performances than index finger gestures by time and accuracy in sitting but similar performances in walking; and (5) for the three implements, gesture drawing time and accuracy on mobile devices reduced from sitting to walking condition. We discuss these findings with implications for future gesture design and research.

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Cited By

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Tu HGao BWu HLyu F(2023)Text Pin: Improving text selection with mode-augmented handles on touchscreen mobile devicesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103028175(103028)Online publication date: Jul-2023
https://doi.org/10.1016/j.ijhcs.2023.103028
Wang MQin GSun MZhao JQin J(2023)An exploration of pressure input with bare finger for Mobile interaction in stationary and Mobile situationsMultimedia Tools and Applications10.1007/s11042-023-14503-082:17(25711-25731)Online publication date: 14-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14503-0
Magrofuoco NRoselli PVanderdonckt J(2022)µV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture RecognizerProceedings of the ACM on Human-Computer Interaction10.1145/35322006:EICS(1-25)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1145/3532200
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Index Terms

Differences and Similarities between Finger and Pen Stroke Gestures on Stationary and Mobile devices
1. Human-centered computing
  1. Human computer interaction (HCI)

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Reviews

Reviewer: Franz J Kurfess

Before the advent of the iPhone, touch-based interaction between users and personal computational devices was dominated by pen-shaped styli, such as in handheld devices by Palm, or in laptops that could be converted to tablet-style devices. Although both pen-based and smartphone-style finger-based interactions rely on touch gestures as input methods, there are differences between the two: a finger has a larger surface area, and is used in a different manner from a stylus. Intuitively, a pen is better for more precise control, whereas fingers are more convenient since they are always at hand. There have been few thorough investigations of the two gesture methods, however. The authors of this publication conducted three experiments with different implements (pen, index finger, and thumb) and postures (sitting versus walking). To facilitate a systematic comparison, they assembled a set of gestures in three categories (simple, medium, complex) and used characteristics like articulation time (start to finish), gesture size ratio (target versus drawn gesture), aperture (between start and end point), corner shape distance (sharp versus broad angles), indicative angle difference ("tilt" between target and drawn gesture), and the proportional shape distance (sum of the distances between sample points of the target and drawn gestures). Overall, the results confirm the intuitive assessments: Stylus gestures perform better for more complex patterns and in smaller interaction areas, and the main advantage of thumb gestures is for one-handed device use. Styli often are available as an alternative input method for smartphones. Interaction designers can benefit from this publication in at least two ways: It is an excellent example of experiments that examine intuitive assumptions, and it contains some concrete recommendations for the appropriate use of stylus- and finger-based gesture interaction. Online Computing Reviews Service

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Information & Contributors

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Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 22, Issue 5

October 2015

217 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/2814459

Editor:
Shumin Zhai
Google, Inc.

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 10 August 2015

Accepted: 01 June 2015

Revised: 01 May 2015

Received: 01 April 2014

Published in TOCHI Volume 22, Issue 5

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Tu HGao BWu HLyu F(2023)Text Pin: Improving text selection with mode-augmented handles on touchscreen mobile devicesInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103028175(103028)Online publication date: Jul-2023
https://doi.org/10.1016/j.ijhcs.2023.103028
Wang MQin GSun MZhao JQin J(2023)An exploration of pressure input with bare finger for Mobile interaction in stationary and Mobile situationsMultimedia Tools and Applications10.1007/s11042-023-14503-082:17(25711-25731)Online publication date: 14-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14503-0
Magrofuoco NRoselli PVanderdonckt J(2022)µV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture RecognizerProceedings of the ACM on Human-Computer Interaction10.1145/35322006:EICS(1-25)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1145/3532200
Xia HGlueck MAnnett MWang MWigdor D(2022)Iteratively Designing Gesture Vocabularies: A Survey and Analysis of Best Practices in the HCI LiteratureACM Transactions on Computer-Human Interaction10.1145/350353729:4(1-54)Online publication date: 5-May-2022
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Sung CCheng PChen L(2022)Effect of colour sensing and drawing pen on children’s colour perception and feature detection of objects when drawingBehaviour & Information Technology10.1080/0144929X.2022.207176442:9(1311-1323)Online publication date: 6-May-2022
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Li QZeng HPeng ZMa X(2021)Understanding Players’ Interaction Patterns with Mobile Game App UI via VisualizationsProceedings of the Ninth International Symposium of Chinese CHI10.1145/3490355.3490357(9-21)Online publication date: 16-Oct-2021
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Tu HHuang JLiang HSkarbez RTian FDuh HKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Distractor Effects on Crossing-Based InteractionProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445340(1-13)Online publication date: 6-May-2021
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Shukri SOmar RRubegni EVasalou AParés NSawhney N(2020)The graphic routines of closed and open shapesProceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts10.1145/3397617.3397844(229-234)Online publication date: 21-Jun-2020
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Akpinar EYeşilada YTemizer S(2020)The Effect of Context on Small Screen and Wearable Device Users’ Performance - A Systematic ReviewACM Computing Surveys10.1145/338637053:3(1-44)Online publication date: 28-May-2020
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Pysal DAbdulkadir SMohd Shukri SAlhussian H(2020)Classification of children’s drawing strategies on touch-screen of seriation objects using a novel deep learning hybrid modelAlexandria Engineering Journal10.1016/j.aej.2020.06.019Online publication date: Jun-2020
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Recommendations

A comparative evaluation of finger and pen stroke gestures

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iWink: Exploring Eyelid Gestures on Mobile Devices

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