Fingers' Range and Comfortable Area for One-Handed Smartphone Interaction Beyond the Touchscreen

Le, Huy Viet; Mayer, Sven; Bader, Patrick; Henze, Niels

doi:10.1145/3173574.3173605

Cited by 55 publications

(5 citation statements)

References 46 publications

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“…For example, we do not have a good idea of what a score of 76 really means or what the participant thinks it means. Moreover, a difference of 73 and 76 could be a meaningful difference, but could equally just be measurement error due to the width of the participant's finger (Boring et al, 2012;Le et al, 2018;Mayer et al, 2018). That said, we can however also not be sure that all participants interpret the response options on Likert scales in the same way, particularly not if some response options have no label.…”

Section: Choosing and Formulating Response Optionsmentioning

confidence: 97%

So you want to do ESM? Ten Essential Topics for Implementing the Experience Sampling Method (ESM)

Fritz,

Piccirillo,

Cohen

et al. 2023

Preprint

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The experience sampling method (ESM) captures psychological experiences over time and in everyday contexts, thereby offering exciting potential for collecting more temporally fine-grained and ecologically valid data for psychological research. Given that rapid methodological developments make it increasingly difficult for novice ESM researchers to be well-informed about standards of ESM research and to identify resources that can serve as useful starting points, this article provides a primer on ten essential design and implementation considerations for ESM studies. Specifically, we i) compare ESM with cross-sectional, panel, and cohort approaches, and discuss considerations regarding: ii) item content and phrasing, iii) choosing and formulating response options, iv) timescale (sampling: frequency, scheme, and duration), v) change properties and stationarity, vi) power and effect sizes, vii) missingness, attrition, and compliance, viii) data assessment and administration, ix) reliability, as well as x) replicability and generalizability. For all ten topics we discuss challenges and – if available – potential solutions, and provide literature that can serve as starting points for more in-depth readings. We also share access to a living, web-based resources library with a more extensive catalogue of literature, to facilitate further learning about the design and implementation of ESM. Lastly, we list topics that were beyond the scope of our article, but can be relevant for the success of ESM studies. Taken together, our article highlights the most essential design and implementation considerations for ESM studies, aids the identification of relevant in-depth readings, and can thereby support the quality of future ESM studies.

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Section: Choosing and Formulating Response Optionsmentioning

confidence: 97%

So you want to do ESM? Ten Essential Topics for Implementing the Experience Sampling Method (ESM)

Fritz,

Piccirillo,

Cohen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…When it refers to the touchscreen, some methods provide users an imaginary zoom in or zoom out glass, the users can touch the desired positions within the glass [15], which is more applicable from tiny to large screens, since when the users shrink a large screen to a touchable size, the keys could be too small to touch accurately. For other components of smartphones, such as Back-of-Device (BoD) touch panels or other sensors, their reachability and usability have been studied previously [16], [17].…”

Section: B Single-handed Text Entrymentioning

confidence: 99%

A Comparative Study on Single-handed Keyboards on Large-screen Mobile Devices

Zhang

Deng

2022

Proceedings of the 2022 International Conference on Advanced Visual Interfaces

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Many questions regarding single-hand text entry on modern smartphones (in particular, large-screen smartphones) remain under-explored, such as, (i) will the existing prevailing single-handed keyboards fit for large-screen smartphone users? and (ii) will individual-customization improve single-handed keyboard performance? In this paper we study single-handed typing behaviors on several representative keyboards on large-screen mobile devices. We found that, (i) the user-adaptive-shape curved keyboard performs best among all the studied keyboards; (ii) users' familiarity with the Qwerty layout plays a significant role at the beginning, but after several sessions of training, the user-adaptive curved keyboard can have the best learning curve and performs best; (iii) generally the statistical decoding algorithms via spatial and language models can well handle the input noise from single-handed typing.

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“…Han et al [23] present a method for automatic labeling of hand markers, to speed up hand tracking for VR. Le et al [38] capture a hand interacting with a phone to study the "comfortable areas", while Feit et al [15] capture two hands interacting with a keyboard to study typing patterns. Other works [37,53] focus on graphics applications.…”

Section: Related Workmentioning

confidence: 99%

GRAB: A Dataset of Whole-Body Human Grasping of Objects

Taheri,

Ghorbani,

Black

et al. 2020

Preprint

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Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.

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Fingers' Range and Comfortable Area for One-Handed Smartphone Interaction Beyond the Touchscreen

Cited by 55 publications

References 46 publications

So you want to do ESM? Ten Essential Topics for Implementing the Experience Sampling Method (ESM)

So you want to do ESM? Ten Essential Topics for Implementing the Experience Sampling Method (ESM)

A Comparative Study on Single-handed Keyboards on Large-screen Mobile Devices

GRAB: A Dataset of Whole-Body Human Grasping of Objects

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