Open-loop and feedback-controlled mouse cursor movements in linear paths

Thibbotuwawa, Namal; Hoffmann, Errol R.; Goonetilleke, Ravindra S.

doi:10.1080/00140139.2011.644587

Cited by 20 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two independent variables were path length A (400, 600, 800, and 1000 pixels) and width W (8,11,15,20, and 26 pixels). The steering law difficulty (A/W ) ranged from 15.4 to 125; these values were higher than 10 so that the W could restrict the speed [57].…”

Section: Designmentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R 2 > 0.92 and MAE < 4.9% for steering and R 2 > 0.95 and MAE < 6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

show abstract

Section: Designmentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

show abstract

“…If the tracking component is ballistic as in (4) and targeting component is under ongoing visual control as in (1), then the overall task (the BV task) will reduce to a targeting task because of the large path width P. Therefore, the ballistic-visual control (BV) task has the form of Fitts' law given in (10).…”

Section: B Modeling the Targeted-tracking Taskmentioning

confidence: 99%

“…It is hypothesized that when the end of the tracking component is approached and ongoing-visual control is no longer necessary to remain within the path, the participant has the opportunity to accelerate the motion to move within the ballistic target as in [10].…”

Section: A Validity Of the Movement Time Modelmentioning

confidence: 99%

See 1 more Smart Citation

Targeted-Tracking With Pointing Devices

Senanayake

Goonetilleke

Hoffmann

2015

IEEE Trans. Human-Mach. Syst.

Self Cite

View full text Add to dashboard Cite

Targeting and tracking in graphical user interfaces have been widely studied, but attempts to model targeted-tracking are few. Targeted-tracking is essentially a two component task of tracking followed by targeting, where either or both components may dominate depending on the levels of difficulty in each component. The applicability of an empirical model based on computer mouse use is unknown with respect to other devices. In order to confirm the model validity for other input devices, experiments were carried out using a mouse, a pen mouse, a touch screen, and a graphics tablet. Fourteen participants were tested on 48 experimental conditions that included four difficulty levels and 12 conditions with varying track width (P), track length (D), and target width (W). Movement time, error rate, index of performance, and throughput were compared. Repeated-measures ANOVA indicated that factors in the targeted-tracking model were significant (p < 0.05) and movement time data were a good fit (R 2 > 0.8) to the model, confirming the generality of the model. A principal component analysis showed that a mouse is relatively superior in terms of both movement time and error rate. Thus, the targetedtracking model is an effective way to compare and evaluate input devices.

show abstract

“…The steering law cannot be applied for paths with low ID values (Drury et al., 1975; Drury and Daniels, 1975; Lin, Drury, & Paquet, 2006) where continuous visual control is not required (Hoffmann, 2009). According to Thibbotuwawa, Hoffmann, and Goonetilleke (2012a), the ID ratio has to exceed a value of around 10, if visual control is to be required to remain within the path. For lower ID values, where the control action is made in open-loop mode, Equation 2 will be valid (Thibbotuwawa et al., 2012a).…”

Section: Introductionmentioning

confidence: 99%

Pointing Device Performance in Steering Tasks

Senanayake

Goonetilleke

2016

Percept Mot Skills

Self Cite

View full text Add to dashboard Cite

Use of touch-screen-based interactions is growing rapidly. Hence, knowing the maneuvering efficacy of touch screens relative to other pointing devices is of great importance in the context of graphical user interfaces. Movement time, accuracy, and user preferences of four pointing device settings were evaluated on a computer with 14 participants aged 20.1 ± 3.13 years. It was found that, depending on the difficulty of the task, the optimal settings differ for ballistic and visual control tasks. With a touch screen, resting the arm increased movement time for steering tasks. When both performance and comfort are considered, whether to use a mouse or a touch screen for person-computer interaction depends on the steering difficulty. Hence, a input device should be chosen based on the application, and should be optimized to match the graphical user interface.

show abstract

Open-loop and feedback-controlled mouse cursor movements in linear paths

Abstract: Many tasks require manoeuvring equipment or devices through a path of limited width. These movements can be made with or without feedback control, depending on the path constraints. The conditions for the two forms of movement are determined in this research.

Cited by 20 publications

References 22 publications

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Targeted-Tracking With Pointing Devices

Pointing Device Performance in Steering Tasks

Contact Info

Product

Resources

About