research-article

Facilitating Text Entry on Smartphones with QWERTY Keyboard for Users with Parkinson’s Disease

Authors:

Ishan Chatterjee,

Yuanchun ShiAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 735, Pages 1 - 12

https://doi.org/10.1145/3411764.3445352

Published: 07 May 2021 Publication History

Abstract

QWERTY is the primary smartphone text input keyboard configuration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson’s disease, can severely affect typing efficiency and user experience. In this paper, we investigated Parkinson’s users’ typing behavior on smartphones. In particular, we identified and compared the typing characteristics generated by users with and without Parkinson’s symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct insertion, substitution and omission errors, while maintaining direct physical interpretation. User study results confirmed that the proposed algorithm outperformed baseline techniques: users reached 22.8 WPM typing speed with a significantly lower error rate and higher user-perceived performance and preference. We concluded that our method could effectively improve the text entry experience on smartphones for users with Parkinson’s disease.

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Cited By

Shi DZhu YJokinen JAcharya APutkonen AZhai SOulasvirta A(2024)CRTypist: Simulating Touchscreen Typing Behavior via Computational RationalityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642918(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642918
Shi YYi XLiang CQin YWang YYan YCheng ZZhu PZhang SLi YLiu YWang XChen JZhou WWang YZhao DDu F(2024)HCI Research and Innovation in China: A 10-Year PerspectiveInternational Journal of Human–Computer Interaction10.1080/10447318.2024.232385840:8(1799-1831)Online publication date: 22-Mar-2024
https://doi.org/10.1080/10447318.2024.2323858
Yi XLiang CChen HSong JYu CLi HShi Y(2023)From 2D to 3DProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808297:1(1-25)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580829
Show More Cited By

Index Terms

Facilitating Text Entry on Smartphones with QWERTY Keyboard for Users with Parkinson’s Disease
1. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
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Aaron Quigley
University of New South Wales, Australia
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Katherine Isbister
University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
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Pernille Bjørn
University of Copenhagen, Denmark
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Microsoft Research, USA

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Published: 07 May 2021

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Cited By

Shi DZhu YJokinen JAcharya APutkonen AZhai SOulasvirta A(2024)CRTypist: Simulating Touchscreen Typing Behavior via Computational RationalityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642918(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642918
Shi YYi XLiang CQin YWang YYan YCheng ZZhu PZhang SLi YLiu YWang XChen JZhou WWang YZhao DDu F(2024)HCI Research and Innovation in China: A 10-Year PerspectiveInternational Journal of Human–Computer Interaction10.1080/10447318.2024.232385840:8(1799-1831)Online publication date: 22-Mar-2024
https://doi.org/10.1080/10447318.2024.2323858
Yi XLiang CChen HSong JYu CLi HShi Y(2023)From 2D to 3DProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808297:1(1-25)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580829
Roy SRoy USinha DPal R(2023)Imbalanced ensemble learning in determining Parkinson’s disease using Keystroke dynamicsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119522217:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119522
Alfalahi HKhandoker AChowdhury NIakovakis DDias SChaudhuri KHadjileontiadis L(2022)Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysisScientific Reports10.1038/s41598-022-11865-712:1Online publication date: 11-May-2022
https://doi.org/10.1038/s41598-022-11865-7
Barnardo LDamasevicius RMaskeliunas R(2022)Using Keytyping as a Biomarker for Cognitive Decline Diagnostics: The Convolutional Neural Network Based ApproachPattern Recognition and Artificial Intelligence10.1007/978-3-031-04112-9_28(367-381)Online publication date: 13-Apr-2022
https://doi.org/10.1007/978-3-031-04112-9_28

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