User-Acceptance of Latency in Touch Interactions

Ritter, Walter; Kempter, Guido; Werner, Tobias

doi:10.1007/978-3-319-20681-3_13

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Combined with the delay introduced by the feature extraction process (300 ms), a grasping could be registered by the system after at least 600 ms from the start of the action. While such a delay might be problematic for some real-time human machine interfaces [ 52 - 54 ], it is not considered to be of concern for the targeted activity monitor application, as the user’s instant action does not depend on the feedback [ 55 - 57 ]. Under the optimal settings, which was based on the smallest maximum percentage error, a median percentage error of 1% with IQR of 2% for the grasping count using the wrist FMG was obtained.…”

Section: Discussionmentioning

confidence: 99%

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Xiao

Menon

2017

JMIR Rehabil Assist Technol

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BackgroundFunctional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated. In contrast to previous studies, this exploratory study investigates the feasibility of (1) detecting grasping when the participants move their arms, which could introduce large artifacts to the point of potentially preventing the practical use of the proposed technology, and (2) counting grasping during arm-reaching tasks.ObjectiveThe aim of this study was to determine the usefulness of FMG in the detection of functional arm movements. The use of FMG straps placed on the forearm is proposed for counting the number of grasping actions in the presence of arm movements.MethodsTen healthy volunteers participated in this study to perform a pick-and-place exercise after providing informed consent. FMG signals were simultaneously collected using 2 FMG straps worn on their wrist and at the midposition of their forearm, respectively. Raw FMG signals and 3 additional FMG features (ie, root mean square, wavelength, and window symmetry) were extracted and fed into a linear discriminant analysis classifier to predict grasping states. The transition from nongrasping to grasping states was detected during the process of counting the number of grasping actions.ResultsThe median accuracy for detecting grasping events using FMG recorded from the wrist was 95%, and the corresponding interquartile range (IQR) was 5%. For forearm FMG classification, the median accuracy was 92%, and the corresponding IQR was 3%. The difference between the 2 median accuracies was statistically significant (P<.001) when using a paired 2-tailed sign test. The median percentage error for counting grasping events when FMG was recorded from the wrist was 1%, and the corresponding IQR was 2%. The median percentage error for FMG recorded from the forearm was 2%, and the corresponding IQR was also 2%. While the median percentage error for the wrist was lower than that of the forearm, the difference between the 2 was not statistically significant based on a paired 2-tailed sign test (P=.29).ConclusionsThis study reports that grasping can reliably be counted using an unobtrusive and simple FMG strap even in the presence of arm movements. Such a result supports the foundation for future research evaluating the feasibility of monitoring hand grasping during unsupervised ADL, leading to further investigations with individuals with motor function deficits of the arm.

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Section: Discussionmentioning

confidence: 99%

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Xiao

Menon

2017

JMIR Rehabil Assist Technol

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“…In terms of the technology, mobile devices are not as powerful as the standard desktop computer used in most research paradigms. The limitations of these devices are well-established (Ng and Dietz, 2014; Ritter et al, 2015; Arsintescu et al, 2017; Begel et al, 2017), and of particular concern is (1) the variability in producing stimuli when requested and (2) the latency of registering touchscreen input. Here, we measured the standard deviation of the IOI to be 14 ms for auditory stimuli and 13 ms for visual stimuli.…”

Section: Discussionmentioning

confidence: 99%

“…Aside from the uncontrolled environment where participants reside, multiple sources of variability may stem from the technology itself, such as different interfaces (e.g., phone, tablet, computer), different operating systems, and different software used to collect data. At a more fundamental level, mobile consumer devices are not necessarily designed to simultaneously produce stimuli and acquire performance data with the precise timing required by typical research projects (Ng and Dietz, 2014; Ritter et al, 2015; Arsintescu et al, 2017; Begel et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Tablet-Based Assessment of Rhythmic Ability

et al. 2019

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The exponential rise in use of mobile consumer electronics has presented a great potential for research to be conducted remotely, with participants numbering several orders of magnitude greater than a typical research paradigm. Here, we attempt to demonstrate the validity and reliability of using a consumer game-engine to create software presented on a mobile tablet to assess sensorimotor synchronization, a proxy of rhythmic ability. Our goal was to ascertain whether previously observed research results can be replicated, rather than assess whether a mobile tablet achieves comparable performance to a desktop computer. To achieve this, younger (aged 18–35 years) and older (aged 60–80 years) adult musicians and non-musicians were recruited to play a custom-designed sensorimotor synchronization assessment on a mobile tablet in a controlled laboratory environment. To assess reliability, participants performed the assessment twice, separated by a week, and an intra-class correlation coefficient (ICC) was calculated. Results supported the validity of this approach to assessing rhythmic abilities by replicating previously observed results. Specifically, musicians performed better than non-musicians, and younger adults performed better than older adults. Participants also performed best when the tempo was in the range of previously-identified preferred tempos, when the stimuli included both audio and visual information, and when synchronizing on-beat compared to off-beat or continuation (self-paced) synchronization. Additionally, high ICC values (>0.75) suggested excellent test–retest reliability. Together, these results support the notion that consumer electronics running software built with a game engine may serve as a valuable resource for remote, mobile-based data collection of rhythmic abilities.

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“…Practically, delays more than 180 ms in FPS game have tended to frustrate users, which lead them to eventually stop playing [3]. Latency of 600 ms for tap actions and 450 ms for drag actions annoyed people using touchscreens [1,43].…”

Section: Effects Of Latency On Human Performancementioning

confidence: 99%

Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games

Lee

Kim

Lee

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Effects of unintended latency on gamer performance have been reported. End-to-end latency can be corrected by postinput manipulation of activation times, but this gives the player unnatural gameplay experience. For moving-target selection games such as Flappy Bird, the paper presents a predictive model of latency on error rate and a novel compensation method for the latency effects by adjusting the game's geometry design-e.g., by modifying the size of the selection region. Without manipulation of the game clock, this can keep the user's error rate constant even if the endto-end latency of the system changes. The approach extends the current model of moving-target selection with two additional assumptions about the effects of latency: (1) latency reduces players' cue-viewing time and (2) pushes the mean of the input distribution backward. The model and method proposed have been validated through precise experiments.

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User-Acceptance of Latency in Touch Interactions

Cited by 8 publications

References 10 publications

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

A Tablet-Based Assessment of Rhythmic Ability

Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games

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