Performance and Ergonomics of Touch Surfaces

Bachynskyi, Myroslav; Palmas, Gregorio; Oulasvirta, Antti; Steimle, Jürgen; Weinkauf, Tino

doi:10.1145/2702123.2702607

Cited by 61 publications

(8 citation statements)

References 27 publications

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“…In this research, we broaden psychophysiological measures to objective measures [ 40 ] as physical activities are important indicators of comfort/discomfort as well, e.g. Brachynskyi et al [ 41 ] evaluated the comfort of sitting postures while using a touchscreen by 1) a motion capture system and 2) a custom built chair which measured the forced applied by the user in various directions. For visual fatigue, the length of saccades, the fixation durations, and features related to blinking collected from eye tracking devices were often used as indicators [ 42 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Desktop lighting for comfortable use of a computer screen

Han

Zhang

Xiang

et al. 2021

WOR

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BACKGROUND: The contrast between a bright computer screen and a dark ambient environment may influence comfort of the users, especially on their eyes. OBJECTIVE: The objective of this research is to identify the optimal desktop lighting for the comfortable use of the computer screen in a dark environment. METHODS: An experiment was designed where seven illumination setups were introduced for the users to perform their leisure tasks on a computer screen. Fifteen healthy subjects participated in the experiments. During each session, durations of the eye blinks, fixations and saccades of the user were recorded by an eye tracker. His/her neck and trunk movements were recorded by a motion tracking system as well. The comfort/discomfort questionnaire, localized postural discomfort questionnaire, NASA task load index and computer user questionnaire were used to record the overall comfort/discomfort, the local perceived physical discomfort, the cognitive workload, and general/eye health problems, respectively. RESULTS: Subjective and objective measurement results indicated that users felt more comfortable with high intensity warm lights using a computer screen. We also identified that the eye fixation durations, as well as the scores of two questions in the computer user questionnaire, have significant negative correlations with comfort. On the other side, the durations of blinks and the scores of three questions in the computer user questionnaire, were significantly correlated with discomfort. CONCLUSION: The warm (3000K) and high intensity (1500 lux) light reduced the visual and cognitive fatigue of the user and therefore improve the comfort of the user during the use of a computer screen.

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Section: Literature Reviewmentioning

confidence: 99%

Desktop lighting for comfortable use of a computer screen

Han

Zhang

Xiang

et al. 2021

WOR

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“…Bi et al improved Fitts' law by introducing the FFitts model for small target acquisition on touchscreens [8]. By varying form factors (tablet, laptop, tabletop, public display, smartphone 2-handed, smartphone 1-handed), Bachynskyi et al measured throughput and muscle activation [5]. They found an average throughput of 6.55bps, with tabletop and 2-handed smartphone throughput about 20% higher and tablet and laptop about 11% lower.…”

Section: Pointingmentioning

confidence: 99%

“…Most research examining touch input performance has implicitly imposed the use of a single finger of the dominant hand [21,34,11,46,8,5,40,39,45,42] or the dominant hand index finger and thumb for object transformation [54,3]. Few studies focus on the accuracy and speed of specific fingers when used individually or when multiple fingers are used together as a chord [15].…”

Section: Introductionmentioning

confidence: 99%

The Performance and Preference of Different Fingers and Chords for Pointing, Dragging, and Object Transformation

Goguey

Nancel

Casiez

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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International audienceThe development of robust methods to identify which finger is causing each touch point, called “finger identification,” will open up a new input space where interaction designers can associate system actions to different fingers. However, relatively little is known about the performance of specific fingers as single touch points or when used together in a “chord.” We present empirical results for accuracy, throughput, and subjective preference gathered in five experiments with 48 participants exploring all 10 fingers and 7 two-finger chords. Based on these results, we develop design guidelines for reasonable target sizes for specific fingers and two-finger chords, and a relative ranking of the suitability of fingers and two-finger chords for common multi-touch tasks. Our work contributes new knowledge regarding specific finger and chord performance and can inform the design of future interaction techniques and interfaces utilizing finger identification

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“…This means action items (in this case the annotation interface) should be within the functional area of the thumb. Since distinctive body postures use distinctive sets of muscles [3], we ensured that at least for standing interaction, asymmetric bimanual input with thumb is comfortable [29].…”

Section: Designing Our Mobile Annotation Methodsmentioning

confidence: 99%

RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth Labels

Zhang

Ali

Wang³

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Collecting accurate and precise emotion ground truth labels for mobile video watching is essential for ensuring meaningful predictions. However, video-based emotion annotation techniques either rely on post-stimulus discrete self-reports, or allow real-time, continuous emotion annotations (RCEA) only for desktop settings. Following a user-centric approach, we designed an RCEA technique for mobile video watching, and validated its usability and reliability in a controlled, indoor (N=12) and later outdoor (N=20) study. Drawing on physiological measures, interaction logs, and subjective workload reports, we show that (1) RCEA is perceived to be usable for annotating emotions while mobile video watching, without increasing users' mental workload (2) the resulting time-variant annotations are comparable with intended emotion attributes of the video stimuli (classification error for valence: 8.3%; arousal: 25%). We contribute a validated annotation technique and associated annotation fusion method, that is suitable for collecting fine-grained emotion annotations while users watch mobile videos.

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Performance and Ergonomics of Touch Surfaces

Cited by 61 publications

References 27 publications

Desktop lighting for comfortable use of a computer screen

Desktop lighting for comfortable use of a computer screen

The Performance and Preference of Different Fingers and Chords for Pointing, Dragging, and Object Transformation

RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth Labels

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