An angular motion Fitt's Law for human performance modeling and prediction

Kondraske, George V.

doi:10.1109/iembs.1994.412031

Cited by 23 publications

(21 citation statements)

References 5 publications

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“…It was found that the IP was similar to that found in translational movements (Crossman & Goodeve, 1983) and that Fitts' law could represent rotational tasks reasonably well. Indeed, the law has been extended to represent elbow flexionextension (Kondraske, 1994) and more recently has been proposed for more complex models of human upper limb performance (Yang, Jin, Zhang, Huang, & Wang, 2001).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Fitts’ Law for Quantifying Combined Rotational and Translational Movements

Stoelen

Akin

2010

Hum Factors

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Objective: To develop a model for human performance in combined translational and rotational movements based on Fitts' law. Background: Fitts' law has been successfully applied to translational movements in the past, providing generalization beyond a specific task as well as performance predictions. For movements involving both translations and rotations, no equivalent theory exists, making comparisons of input devices for these movements more ambiguous. Method: The study consisted of three experiments. In the first two, participants performed either pure translational or pure rotational movements of 1 degree of freedom. The third experiment involved the same movements combined. Results: On average, the performance times for combined movements were equal to the sum of the times for equivalent separate rotational and translational movements. A simple Fitts' law equivalent for combined movements with a similar slope as the separate com-ponents was proposed. In addition, a significant degree of coordination of the combined movements was found. This had a strong bias toward a parallel execution in 12 out of 13 participants. Conclusion: Combined movements with rotations and translations of 1 degree of freedom can be approximated using a simple Fitts' law equivalent. The rotational and translational components appear to be coordinated by the central nervous system to generate a parallel execution. Application: The results may help drive human interface designs and provide insights into the coordination of combined movements. Future extensions may be possible for the movements of higher degrees of freedom used in robot teleoperation and virtual reality applications.

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Section: Introductionmentioning

confidence: 99%

Assessment of Fitts’ Law for Quantifying Combined Rotational and Translational Movements

Stoelen

Akin

2010

Hum Factors

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“…The angular size of a target from the viewpoint of the user is more meaningful. Hence, we measure D and W in angular degrees, as proposed in earlier work [4].…”

Section: Magic Lens Pointing: Applying the Model Of Rohs And Oulasvirmentioning

confidence: 99%

Interaction with magic lenses

Rohs

Oulasvirta

Suomalainen

2011

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Rohs and Oulasvirta (2008) proposed a two-component Fitts' law model for target acquisition with magic lenses in mobile augmented reality (AR) with 1) a physical pointing phase, in which the target can be directly observed on the background surface, and 2) a virtual pointing phase, in which the target can only be observed through the device display. The model provides a good fit (R 2 =0.88) with laboratory data, but it is not known if it generalizes to realworld AR tasks. In the present outdoor study, subjects (N=12) did building-selection tasks in an urban area. The differences in task characteristics to the laboratory study are drastic: targets are three-dimensional and they vary in shape, size, z-distance, and visual context. Nevertheless, the model yielded an R 2 of 0.80, and when using effective target width an R 2 of 0.88 was achieved.

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“…Fitts' Law describes well the relationship of the assigned angle and the difficulty with selection [5] [6]. According to the law, the index of difficulty is expressed as a log function of angular movement and angular target width and it is proportional to the time for selection.…”

Section: Target Searching With Graphical Feedback and Its Problemsmentioning

confidence: 99%

ITHROW : A New Gesture-Based Wearable Input Device with Target Selection Algorithm

Yoo

Jeong

Song

et al. 2007

2007 International Conference on Machine Learning and Cybernetics

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We present an intelligent interface system which includes a new gesture-based wearable input device, called iThrow, as a main user interface for mobile devices, and an infrastructure helping users be aware of and make a use of various public devices in user-friendly manners. In this kind of intelligent interface system, selecting an object among multiple ones is one of the fundamental functions because it is a pre-cursor to all other subsequent actions. We propose a new selection algorithm which improves selection speed by adaptively resizing the objects' angular widths. Results show that the proposed algorithm outperforms the ray-based selection technique in selection speed about 62.6%.

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An angular motion Fitt's Law for human performance modeling and prediction

Cited by 23 publications

References 5 publications

Assessment of Fitts’ Law for Quantifying Combined Rotational and Translational Movements

Assessment of Fitts’ Law for Quantifying Combined Rotational and Translational Movements

Interaction with magic lenses

ITHROW : A New Gesture-Based Wearable Input Device with Target Selection Algorithm

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