Application of Fitts' Law to Foot-Pedal Design

1975 ◽  
Vol 17 (4) ◽  
pp. 368-373 ◽  
Author(s):  
Colin G. Drury
Keyword(s):  
Movement Time ◽  
Hand Movements ◽  
Slight Effect ◽  
Fitts Law ◽  
Foot Tapping ◽  
Direction Of Movement

Two experiments on reciprocal foot tapping between pedals showed that a modified version of Fitts' Law can predict movement time for a variety of pedal sizes and separations. Using a relationship between times for reciprocal tapping and single movements found for hand movements, the present results predict closely the movement times obtained under specific conditions by earlier researchers. When pedals are at minimum safe separation it is concluded that pedal widths and direction of movement have only a slight effect on movement time.

scholarly journals Both Sides Now: Both Height and Width of the Target Matter for Applying Fitts’ Law to Pointing Using a Mouse

2019 ◽  
Vol 63 (1) ◽  
pp. 381-385
Author(s):  
Douglas J. Gillan
Keyword(s):  
User Interface ◽  
Interface Design ◽  
Movement Time ◽  
User Interface Design ◽  
Task Environment ◽  
Target Height ◽  
Fitts Law ◽  
Starting Point ◽  
Direction Of Movement ◽  
Index Of Difficulty

Research on target acquisition has focused on two features of the task environment – the distance moved and the size of the target in the direction of movement. The present research examined the effect of the size of the target in the direction orthogonal to the direction of movement (typically called the target height) on the time to move to a target. The experiment varied the Index of Difficulty (ID) (by varying the distance moved and the target width) and the target height in a task in which participants moved a cursor from a starting point to the target. The results found that (1) movement time was linearly related to ID at each of three levels of target height, (2) movement time increased as target height decreased, and (3) the slope of the function relating movement time to ID decreased as target height decreased. The discussion addresses two possible explanations for the results, how Fitts’ Law might be modified to take target height into account, and how the results could be applied to user interface design.

Fitts' Law in 3D Space with Coordinated Hand Movements

2012 ◽  
Author(s):  
Xiaolu Zeng ◽  
Alan Hedge ◽  
Francois Guimbretiere
Keyword(s):  
Hand Movements ◽  
Fitts Law ◽  
3D Space

Movement Time to an Array of Controls

1993 ◽  
Vol 37 (9) ◽  
pp. 625-629 ◽  
Author(s):  
Errol R. Hoffmann
Keyword(s):  
Mathematical Model ◽  
Theoretical Result ◽  
Movement Time ◽  
Element Model ◽  
Optimum Control ◽  
Fitts Law ◽  
Model Based ◽  
Array Density

Two tasks in which subjects aim at an array of devices were considered: moving to one knob within an array and moving the finger on a numeric keypad. It was shown by a mathematical model based on Fitts' law, that when the array density is specified for the array of knobs or keys, there is an optimum control size for minimum movement time. The theoretical result was obtained by considering a two-element model of the movement, the first being a reach to the general location of the control and the second describing the insertion of the fingers into the space between adjacent controls. As the first element has a movement time that decreases with increase of control size and the second a time increasing with control size, there is an optimum control size where the movement time is a minimum.

Factors Affecting Remote Positioning Performance

1995 ◽  
Vol 39 (4) ◽  
pp. 282-286
Author(s):  
Shang H. Hsu ◽  
Chien C. Huang
Keyword(s):  
Movement Time ◽  
Interactive Effect ◽  
Vertical Movement ◽  
Movement Direction ◽  
Movement Amplitude ◽  
Distance Ratio ◽  
Factors Affecting ◽  
Fitts Law ◽  
Movement Distance ◽  
Two Measures

The purpose of this study was to investigate the effects of target width, movement direction, movement amplitude, and remote distance on remote positioning performance. Movement time and movement distance ratio were taken as measures of remote positioning performance. It was found that the effects of target width, movement amplitude, and movement direction on the two measures were significant. The effect of remote distance was significant only for movement distance ratio. The magnitude of the effect of target width on movement time was larger than that of movement amplitude; a modification of Fitts' Law was thus proposed. Moreover, there was an interactive effect between target width and movement direction- i.e., movement direction had an effect only when the target width was small. Among the eight movement directions, upward vertical movement was the best for remote positioning. The results shed some light onto the design of remote control user interface.

What is Fitts' law about?

1997 ◽  
Vol 20 (2) ◽  
pp. 312-313
Author(s):  
Herbert Heuer
Keyword(s):  
Movement Time ◽  
Fitts Law ◽  
Kinematic Theory

Movement time and accuracy, as defined by Plamondon & Alimi, do not conform to empirical definitions. When definitions are used that conform better to empirical ones, the original predictions of the kinematic theory are no longer valid – as is demonstrated by simulations. Thus the theoretically derived quadratic law and the successful empirical quadratic law seem to be independent of each other.

Foot Placement and Response Latency: A Test of Fitts' Law

1980 ◽  
Vol 24 (1) ◽  
pp. 626-628 ◽  
Author(s):  
Howard J Glaser ◽  
Charles G. Halcomb
Keyword(s):  
Response Latency ◽  
Movement Time ◽  
Three Dimensional ◽  
Vertical Plane ◽  
Brake Pedal ◽  
Lateral Separation ◽  
Response Latencies ◽  
Foot Placement ◽  
Fitts Law ◽  
Experimental Response

Response latencies were compared for three-dimensional brake/accelerator placements (depth, height, and lateral separation). Brake pedal width was found to be the only factor significantly affecting movement time. A comparison between Fitts' and Welford's movement time predictions and experimental response latencies resulted in correlations of .549 (p<.0001) and .543 (p<.0001). Neither movement time equations were able to predict response latencies when the brake pedal was placed 2.54 cm behind the vertical plane of the accelerator. Fitts' and Welford's equations are seen to have limited use in predicting three-dimensional foot movements.

Effects of Gain and Index of Difficulty on Mouse Movement Time and Fitts’ Law

2019 ◽  
Vol 49 (6) ◽  
pp. 684-691
Author(s):  
Yik Hang Pang ◽  
Errol R. Hoffmann ◽  
Ravindra S. Goonetilleke
Keyword(s):  
Movement Time ◽  
Fitts Law ◽  
Mouse Movement ◽  
Index Of Difficulty

Effects of Smoking Abstinence on Movement Regulation

2000 ◽  
Vol 90 (2) ◽  
pp. 624-630 ◽  
Author(s):  
Thomas S. Marzilli ◽  
John B. Shea
Keyword(s):  
Information Processing ◽  
Psychomotor Performance ◽  
Movement Time ◽  
Smoking Abstinence ◽  
Fitts Law ◽  
Single Plate ◽  
Index Of Difficulty ◽  
Psychomotor Tasks ◽  
Nicotine Deprivation ◽  
Minimal Information

The effect of smoking abstinence on performance of a reciprocal tapping task was investigated. 6 habitual smokers performed a single-plate and two versions of a two-plate tapping task. Fitts' Law was used to compute an index of difficulty (ID) in bits for the tasks which was 0 bits for the single-plate and 3.32 and 4.17 bits for the two-plate versions of the task. While smoking abstinence had no effect on performance of the single-plate tapping task, it increased movement time on performance of both two-plate task versions. These findings may provide a coherent explanation for the prior findings of nicotine deprivation on psychomotor performance in the literature. This explanation suggests that the effects of nicotine deprivation as incurred through smoking abstinence may be on the central mechanisms regulating information-processing rate for successful movement regulation. Thus nicotine deprivation may not affect performance of simple psychomotor tasks which require minimal information processing but will affect the performance of more complex tasks requiring significantly more information processing for successful movement regulation.

scholarly journals Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol

2018 ◽  
Vol 119 (6) ◽  
pp. 2030-2035 ◽  
Author(s):  
Cassie N. Borish ◽  
Adam Feinman ◽  
Matteo Bertucco ◽  
Natalie G. Ramsy ◽  
Terence D. Sanger
Keyword(s):  
Movement Time ◽  
Control Input ◽  
Bayesian Filtering ◽  
Success Rates ◽  
Bayesian Algorithm ◽  
Signal Processing Algorithm ◽  
Bayesian Filter ◽  
Fitts Law ◽  
Communication Devices ◽  
Speed Accuracy

Nonlinear Bayesian filtering of surface electromyography (EMG) can provide a stable output signal with little delay and the ability to change rapidly, making it a potential control input for prosthetic or communication devices. We hypothesized that myocontrol follows Fitts’ Law, and that Bayesian filtered EMG would improve movement times and success rates when compared with linearly filtered EMG. We tested the two filters using a Fitts’ Law speed-accuracy paradigm in a one-muscle myocontrol task with EMG captured from the dominant first dorsal interosseous muscle. Cursor position in one dimension was proportional to EMG. Six indices of difficulty were tested, varying the target size and distance. We examined two performance measures: movement time (MT) and success rate. The filter had a significant effect on both MT and success. MT followed Fitts’ Law and the speed-accuracy relationship exhibited a significantly higher channel capacity when using the Bayesian filter. Subjects seemed to be less cautious using the Bayesian filter due to its lower error rate and smoother control. These findings suggest that Bayesian filtering may be a useful component for myoelectrically controlled prosthetics or communication devices. NEW & NOTEWORTHY Whereas previous work has focused on assessing the Bayesian algorithm as a signal processing algorithm for EMG, this study assesses the use of the Bayesian algorithm for online EMG control. In other words, the subjects see the output of the filter and can adapt their own behavior to use the filter optimally as a tool. This study compares how subjects adapt EMG behavior using the Bayesian algorithm vs. a linear algorithm.

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