Division of Information Management Engineering User Interface Laboratory A Model for Movement Time on Data-entry Keyboards Colin G. Drury And Errol R.

Slides:



Advertisements
Similar presentations
Brief introduction on Logistic Regression
Advertisements

RELIABILITY Reliability refers to the consistency of a test or measurement. Reliability studies Test-retest reliability Equipment and/or procedures Intra-
Analysis of variance (ANOVA)-the General Linear Model (GLM)
Chapter 10 Regression. Defining Regression Simple linear regression features one independent variable and one dependent variable, as in correlation the.
Regression and Correlation
Regression line – Fitting a line to data If the scatter plot shows a clear linear pattern: a straight line through the points can describe the overall.
SIMPLE LINEAR REGRESSION
Objectives Define predictive and descriptive models and explain why they are useful. Describe Fitts’ Law and explain its implications for interface design.
REGRESSION AND CORRELATION
Regression Chapter 10 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.
SIMPLE LINEAR REGRESSION
1 Chapter 17: Introduction to Regression. 2 Introduction to Linear Regression The Pearson correlation measures the degree to which a set of data points.
Chapter 7 Correlational Research Gay, Mills, and Airasian
Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: prediction Original citation: Dougherty, C. (2012) EC220 - Introduction.
Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. More About Regression Chapter 14.
Smith/Davis (c) 2005 Prentice Hall Chapter Eight Correlation and Prediction PowerPoint Presentation created by Dr. Susan R. Burns Morningside College.
1 PREDICTION In the previous sequence, we saw how to predict the price of a good or asset given the composition of its characteristics. In this sequence,
SIMPLE LINEAR REGRESSION
F ITTS ’ L AW ○A model of human psychomotor behavior ○Human movement is analogous to the transmission of information ○Movements are assigned indices of.
Effect of Mutual Coupling on the Performance of Uniformly and Non-
CPE 619 Simple Linear Regression Models Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama.
Lecture Presentation Software to accompany Investment Analysis and Portfolio Management Seventh Edition by Frank K. Reilly & Keith C. Brown Chapter 7.
Biostatistics Unit 9 – Regression and Correlation.
Introduction ANOVA Mike Tucker School of Psychology B209 Portland Square University of Plymouth Drake Circus Plymouth, PL4 8AA Tel: +44 (0)
Copyright © 2010 Pearson Education, Inc Chapter Seventeen Correlation and Regression.
Name: Angelica F. White WEMBA10. Teach students how to make sound decisions and recommendations that are based on reliable quantitative information During.
Quality of Curve Fitting P M V Subbarao Professor Mechanical Engineering Department Suitability of A Model to a Data Set…..
The Examination of Residuals. Examination of Residuals The fitting of models to data is done using an iterative approach. The first step is to fit a simple.
Geographic Information Science
Testing Hypotheses about Differences among Several Means.
Examining Relationships in Quantitative Research
1 Chapter 12 Simple Linear Regression. 2 Chapter Outline  Simple Linear Regression Model  Least Squares Method  Coefficient of Determination  Model.
Brian Macpherson Ph.D, Professor of Statistics, University of Manitoba Tom Bingham Statistician, The Boeing Company.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Prof Jim Warren with reference to sections 7.4 and 7.6 of The Resonant Interface.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
This material is approved for public release. Distribution is limited by the Software Engineering Institute to attendees. Sponsored by the U.S. Department.
1© Manhattan Press (H.K.) Ltd. Measurements and errors Precision and accuracy Significant figures cientific notation S cientific notation Measurements.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
11/23/2015Slide 1 Using a combination of tables and plots from SPSS plus spreadsheets from Excel, we will show the linkage between correlation and linear.
Regression Analysis © 2007 Prentice Hall17-1. © 2007 Prentice Hall17-2 Chapter Outline 1) Correlations 2) Bivariate Regression 3) Statistics Associated.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Model Building and Model Diagnostics Chapter 15.
Chapter 10 Correlation and Regression Lecture 1 Sections: 10.1 – 10.2.
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
Correlation & Regression Analysis
1 Module One: Measurements and Uncertainties No measurement can perfectly determine the value of the quantity being measured. The uncertainty of a measurement.
Smith/Davis (c) 2005 Prentice Hall Chapter Fifteen Inferential Tests of Significance III: Analyzing and Interpreting Experiments with Multiple Independent.
Korea University User Interface Lab Copyright 2008 by User Interface Lab Human Action Laws in Electronic Virtual Worlds – An Empirical Study of Path Steering.
1 HETEROSCEDASTICITY: WEIGHTED AND LOGARITHMIC REGRESSIONS This sequence presents two methods for dealing with the problem of heteroscedasticity. We will.
CORRELATION ANALYSIS.
Biostatistics Regression and Correlation Methods Class #10 April 4, 2000.
EXCEL DECISION MAKING TOOLS AND CHARTS BASIC FORMULAE - REGRESSION - GOAL SEEK - SOLVER.
Character legibility: which details are important, and how might it be measured? John Hayes, Jim Sheedy, Yu-Chi Tai, Vinsunt Donato, David Glabe.
5. Evaluation of measuring tools: reliability Psychometrics. 2011/12. Group A (English)
Deep Feedforward Networks
J. A. Landsheer & G. van den Wittenboer
Regression and Correlation
Chapter 5 STATISTICS (PART 4).
Estimating with PROBE II
Suppose the maximum number of hours of study among students in your sample is 6. If you used the equation to predict the test score of a student who studied.
Evaluation of measuring tools: reliability
Limitations for manual and telemanipulator-assisted motion tracking—implications for endoscopic beating-heart surgery  Stephan Jacobs, MD, David Holzhey,
Responses of Collicular Fixation Neurons to Gaze Shift Perturbations in Head- Unrestrained Monkey Reveal Gaze Feedback Control  Woo Young Choi, Daniel.
Product moment correlation
An Introduction to Correlational Research
DSS-ESTIMATING COSTS Cost estimation is the process of estimating the relationship between costs and cost driver activities. We estimate costs for three.
Fitts’s Law Incredibly professional presentation by Thomas Gin, someone please hire me.
ENM 310 Design of Experiments and Regression Analysis Chapter 3
14 Design of Experiments with Several Factors CHAPTER OUTLINE
A simple rule of thumb for elegant prehension
Presentation transcript:

Division of Information Management Engineering User Interface Laboratory A Model for Movement Time on Data-entry Keyboards Colin G. Drury And Errol R. Hoffmann UI 연구실 백지승

Division of Information Management Engineering User Interface Laboratory INTRODUCTION 2.A model for optimum layout of keyboards 3.Experiment 1. Test of model with simulated keyboard 3.1 Subjects 3.2 Experimental conditions 3.3 Results 4.Experiment 2. Movement times on a real keyboard 4.1 Subjects 4.2 Apparatus 4.3 Procedure 4.4 Results and discussion 5.Experiment 3. Movement times on a calculator keyboard 6.Survey of keyboard devices 7.Conclusions Contents

Division of Information Management Engineering User Interface Laboratory 18 3 Very little research has been reported on modelling of keyboard motions Standard layouts have been developed with little consideration of geometrical factors affecting performance Performance measures of keying time and accuracy had perfect rank-order correlation for the eight different keyboards This paper presents a model and experiments which go some way towards rectifying this situation for the simplest case of data-entry keyboards 1. Introduction

Division of Information Management Engineering User Interface Laboratory 18 4 A model shows the dependence of movement time (MT) on key size spacing S=centre-to-centre key spacing B= key width C=distance between key edges F=finger width (used interchangeably with P=probe width, where appropriate) Task: to hit the required key while at the same time missing the surrounding keys 2 cases that need to be considered → where the spacing between the key edges is respectively greater and less than the finger width 2. A model for optimum layout of keyboards Fig. 1 The two cases of keyboard lay out considered in the model for performance Case 1 : for distance between the keys greater than the finger width Case 2 : when the finger is wider than the distance between keys * the fingers are 'square-ended‘

Division of Information Management Engineering User Interface Laboratory Case 1. Limited by missing the target key occurs when C>F or S>B+ F → adjacent keys do not interfere with the aiming task effective target width :W= B+ F Movement Time for this case, MT=a+b log2 [2kS/(B+F)}(1) maximum to minimize the movement time occurs when C=F or S=B+F MT=a+b log2 [2k](2) 2.1 Case 2. Limited by hitting adjacent keys S<B+F and the effective target width is given by W= 28-B-F If Fitts' Law is applicable, the MT is given by MT=a+b log2 [2kS/(2S-B-f)]  B ↑ ⇒ the index of difficulty (ID =log2 (2 x amplitude/target tolerance))  B should be small → S=B+F ⇒ MT=a+b log2 [2k] 2. A model for optimum layout of keyboards

Division of Information Management Engineering User Interface Laboratory 18 6 The minimum movement time : when C=F The variation of the movement time comes from a variation of the ID at constant S, due to the changes in effective target width The maximum effective width is seen at the intersection of the lines describing the two cases; at this point C=F. If the keys are space-filling (B=S) ⇒ case 2 is applicable and W=B- F keys give a minimum effective target width and hence a maximum MT for the given spacing Fig.2 Demonstration of the effective target width as a function of the key width, and the optimum width predicted by the model 2. A model for optimum layout of keyboards

Division of Information Management Engineering User Interface Laboratory Subjects Ten male subjects, ranging in age from 18 to 27 years Finger pad size was measured for each subject by inking the index finger and having the subject lightly press the finger onto paper in the posture used in making the movements. Measured finger pad size : 10 ~ 12 mm, mean value : 11 mm 3.2 Experimental conditions The spacing of the target and constraining adjacent strips : constant at 20 mm the target width was varied to vary the ClF ratio. The amplitude of movement (A) : 160 mm Four metal probes widths: 0, 5, 10, 15 mm / target width : 2, 6, 10, 14, 18 mm F=5 mm: CIF=0.4 to 3.6 F= 10 mm: CIF=0.2 to 1.8 F= 15 mm: CIF= 0.13 to 1.2 Each subject had a different random order of presentation of probes within target size 3. Experiment 1. Test of model with simulated keyboard

Division of Information Management Engineering User Interface Laboratory Results Analysis of Variance Analysis of variance ⇒ target width, probe width and target condition Significant interactions btw. target width and probe width, target width and target cond ition and probe width and target condition for the narrowest probes width: significant differences btw. the target widths for larger probe width : no significant effect of the target width 3. Experiment 1. Test of model with simulated keyboard Table I. Mean movement times (ms) for the various target and probe widths in the single and triple target tasks of experiment 1. Upper value in each case is for single targets, lower value for triple targets.

Division of Information Management Engineering User Interface Laboratory 18 9 At higher target widths, the differences in MT become significant This interaction clearly shows the two effects: target width ↑ ⇒ the single target movement time continuously ↓ the triple target condition first shows ↓ → ↑ Considering only the four metal probes, there was no significant difference in the movement times of single/triple targets for the pointed probe The difference becomes significant : 5 mm → 10mm → 15 mm probe 3. Experiment 1. Test of model with simulated keyboard Fig. 3 Fig. 4

Division of Information Management Engineering User Interface Laboratory Results Regression analysis: single targets carried out using a modified form of equation 1, in which the proportion of added probe width (E) to yield maximum was determined MT=a+b log2 [2A/(B+EP)] The maximum occurred when E=0.60 MT= ; =0.962 (3) the regression was not greatly sensitive to the value of E maximum correlation occurred when a finger pad width of 10 mm was used MT= ; = 0·98 (4) 3. Experiment 1. Test of model with simulated keyboard Fig. 5

Division of Information Management Engineering User Interface Laboratory Results Regression analysis: triple targets form of movement time as a function of the ratio of C/F=clearance between keys/probe width. assumed that the full finger width is added to make up the effective target width each probe width shows the same patterns of movement time; firstly a decrease with increase of C/F and then an increase with C/F values greater than unity 3. Experiment 1. Test of model with simulated keyboard Fig.6Fig.7 The simple explanation of this behavior lies in the way in which the effective index of difficulty changes with effective target width for the various experimental conditions MT= (ID); =0.62 (5) Assumption of a finger pad width of 10 mm shows a minimum movement time at the correct location of C/F = 1, however the variation of MT was small The maximum variation was 11 ms, with the metal probe case of P= 10 mm Fig.8 The other strange feature of this data is that at the most extreme values of C/F, the data show a decrease in movement time, although not to the extent of the minimum MT The model needs to be tested on real keyboards in order to determine its validity under more realistic conditions

Division of Information Management Engineering User Interface Laboratory Subject Ten subjects(six male) ranging in age from 16 to 45 years 4.2 Apparatus Five boards were built on a standard 19mm matrix board. Each of these keyboards had a set of five keys, set in a "cross' pattern, at one end, th e centre one of which was the target key Key caps were machined to have square tops of sizes 2, 6, 10, 14, and 18 mm Starting keys were set at spacings of 2, 4, 6, and 8 keys from the target key The experiment was similar to experiment 1 except : (i) the finger was used (ii) different amplitudes of movement were used (iii) square target keys were used The aim of the experiment ⇒ not only to test the model for real keyboards, but also to determine the effect of different levels of index of difficulty 4.3 Procedure 4. Experiment 2. Movement times on a real keyboard

Division of Information Management Engineering User Interface Laboratory Results and discussion Measurement of effective target width 4. Experiment 2. Movement times on a real keyboard Fig. 9 the optimum key widths for each subject have been determined from plots similar to figure 9 Note that for subject 9, who had a very wide finger, the optimum key width is less than 2 mm The mean values of MT in table 1 indicate that the effective target width is a maximum at a key width of 10 mm; the mean optimum key width is however about 7 mm (table 2). Table 2

Division of Information Management Engineering User Interface Laboratory Results and discussion Measurement of effective target width The model prediction for the optimum key size, for a given key spacing B=S-0.5( - ) (6) Regression of effective target width as a function of key width and finger pad width gave (17 cases) = B+1.73F; =0.32(7) the data for case 2 effective target widths were well represented by the model predictions, yielding (33 cases) = B-1.33F; (8) The optimum key width for this group of subjects = F (9) 4. Experiment 2. Movement times on a real keyboard

Division of Information Management Engineering User Interface Laboratory Results and discussion Movement times 4. Experiment 2. Movement times on a real keyboard The mean data showed a very weak, and non-significant, minimum in the movement ti me at a key size of 6 mm Movement times on real keyboards (experiment 2), showing the effect of key size and amplitude of movement. Indexes of difficulty have been calculated for each experimental condition using the mean values of effective target width in table 2. A plot of movement times as a function of this 10 is shown in figure 11. The relationship is MT= (ID); =0.93 (10) Fig.10Fig.11 Table 3 Key size (mm) at which minimum movement time occurred as a function of amplitude and direction of movement, for each of the ten subjects in experiment 2 The overall mean minimum movement time occurs at a key width of about 8 mm Fig. 12 Error rate was controlled in the experiment so that no more than one error occurred in a give condition The total percent of errors, averaged over the ten subjects along with the mean movement time The figure shows that a minimum error rate occurs at B=6 mm, which is in agreement with the location of the minimum in movement time

Division of Information Management Engineering User Interface Laboratory A small experiment was carried out using an HP35 calculator Measuring the number of movements made between various keys on the board over a period of 10 s. Ten subjects between the ages of 14 ~ 55 It is seen that the classic form of Fitts' Law is obtained, with levelling-off of MT at low values of 10 Fitts' Law cannot be simply applied to the calculation of keyboard movement times as, at low 10 values, the MT will be underestimated Above an index of difficulty of about three, the MTs show a linear increase with Experiment 3. Movement times on a calculator keyboard Fig.13

Division of Information Management Engineering User Interface Laboratory Survey of keyboard devices In order to compare current practice with the results of these experiments, a survey of electronic devices containing keyboards was undertaken Many of these devices had spacing and key size which differed in the vertical and horizontal directions, thus results of the survey are presented separately for the two directions The upper line on each of these figures is for B=S, that is, for space-filling keys The lower line is for B=S-10 mm, which would be about the optimum key width with keys of small deflection

Division of Information Management Engineering User Interface Laboratory 18 A model for keyboard movement times shows that, for a given key spacing, there is an optimum key size for minimum movement times Experiments to determine the effective finger width indicate that, when there is a single target, the effective target size is close to the sum of the target width and the finger width Experiments on real keyboards with subjects using their fingers showed a minimum in both movement time and error rate at a key width of about 6 to 8 mm, when the key spacing was 19 mm. The width of the subject's finger was important in determining the optimum key size, but despite this variability, an optimum could be demonstrated 7. Conclusions