A HXGH PERFORMANCE SOFT KEYBOARD FOR MOBILE SYSTEMS

A Thesis

Presented to

The Facuity of Graduate Studies

of

The University of Guelph

BY

S H A W XIA ZHANG

In partial fulfillment of requirement

for the degree of

Master of Science

February, 1998

O Shawn Xia Shang, 1998

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ABSTRACT

A HIGH PERFORMANCE SOFT KEXBOARD FOR MOBILE SYSTEMS

S h a w X. Zhang

University of Guelph, 1998

Supervisor:

Professor 1. S. MacKenzie

The major issues in text entry on mobile cornputer systems are presented. Two formai

usability experiments were conducted to explore particular problems in the design and

usability of high performance soft keyboards.

An experiment with 12 participants compared text entry rates on large and small sofi

keyboards with QWERTY layouts. Participants were also given a standard touch typing

test. The resuits showed that touch typing skills on physical keyboards do not necessarily

transfer to stylus tapping on a soft keyboard. Participants touch typing speeds ranged

fiom 19 to 74 wpm, whereas their stylus tapping speeds ranged from 15.4 to 25.0 wprn

on a large sol3 keyboard and from 13.1 to 26.0 wprn on a small soft keyboard. The

correlations were low: .416 for the large soft keyboard and .523 for the smdl soft

keyboard.

We also designed a completely new high performance sofi keyboard, based on a

linguistic model, Fitts' law, and other rnodels. The new design supports a predicted

maximum text entry speed of 58.2 wpm. This is about 35% faster than with a QWERTY

layout (at a maximum of about 43.2 wprn).

A second longitudinai text-entry experiment with five participants was conducted to

compare our hi& performance soft keyboard with a QWERW sofi keyboard. The

results showed that, with practice, users can perfom better with the new design. The

crossover point occurred at the 1 0 ~ session of the 20-session experiment. This

corresponds to about 3 hours of practice. The error rates were significantly Iower with

the high performance layout than with the QWERTY layout, and this was consistent

throughout the experiment. Over the coune of the experiment, mean entry speeds

improved, finishing at 44.3 wpm for the high performance layout design, and 40.3 wpm

for the QWERTY Iayout. A regression model based on the power law of leamhg

predicts that at session 50, the mean entry rates would reach 60.7 and 44.8 wpm, for the

high performance and QWERTY Iayouts, respectively. These are very close to the rates

predicted in our model.

To Hua, Lili and Our parents.

Acknowledgement

1 wish to thank my thesis advisor Dr. Scott MacKenzie for his knowledgeable and skillfùl

guidance and supervision during my study. It was his knowledge, enthusiasm and

achievement in human computer interaction that made me interested in the area of HCI.

Working with him was also a great pleasure.

1 would also like to thank Dr. Fangju Wang, for serving in my supervisory comrnittee.

Many thanks to Dr. Tom Wilson who chaired my thesis examination comrnittee.

Thanks are given to the members of the Input Research Group - Guelph, Input Research

Group - Toronto, and al1 who have contributed in this research, in big way or small way.

Thanks are aiso given to my fellow graduate student and fnend William Soukoreff, for

sharing his knowledge and helping me in many ways in my study.

My greatest thanks go to my wife Hua Shen for her love and unmatched support, patience

and encouragement. My daughter Lili brightened my days with her love and cheers, and

sometimes a littie rnischief. Thanks to my parents for dways being there with me.

Table of Content

Acknowledgement ............................................................................................................. i . . Table of Content ................................................................................................................. ii

List of Figures .................................................................................................................... iv ....................................................................................................... Chapter 1 . Introduction 1

. . 1 1. Mobile Computing ..................................... ... 1 7 1.2. Text Entry with MobiIe Systems ........................................................................... -

1.2.1. How Pen-Based Computers Work .................................................................... 4 . . . . 1.2.2. Handwntmg Recognition .................................................................................. 5 1.2.3. Physical Keypads and Sofi Keyboards ............................................................. 5

1.2.3.1. Physical and Safi Keypads ...................................................................... 6 1.2.3.2. Soft Keyboards ......................................................................................... 9

.............................................................................................. 1.3. Focus of this Work 11 ............................................................................................ Chapter 2 . Literature Review 12

2.1. Text Entry Using Recognition Software ............................................................. 12 2.1.1. Character-Based Recognition ......................................................................... 13 2.1.2. Word-Based Recognition ............. .... ........................................................... 16

2.2. Text Entq Using Sofl Keyboards ..................................................................... 17 CC 97 . 2.3. The Elusive Crossover Pont ............................................................................ 23

............................................................................ 2.4. ModeIs ,., ............................ 24 2.4.1. The Linguistic Mode1 and Space Key Extension ............................................ 24 2.4.2. Fitts' Law ......................................................................................................... 26

................................................................................ 3.4.3. Visual Scan Time Mode1 28 ................................................................................. 2.4.4. Saccadic Eye Movement 29

2.4.5. Soukoreff and MacKenzie's Optimal Angle Mode1 ........................................ 30 ............................................................................................ 2.4.6. Key Repeat Time 32

Chapter 3 . The Design of Soft Keyboards ...................................................................... 34 3.1. Issues Related to Sofi Keyboard Design ............................................................. 34

3.1.1. Visual Scan: Fixed vs . Random .................................................................... 34 3.1.2. Ski11 Transfer from Previous Experience ....................................................... 35 3.1.3. Size: Large vs . Small ................................................................................... 36

............................................................................................... 3.1.4. Leaming Mode1 36 3.1 .5 . Possibilities for Alternatives ........................................................................... 37

....................................................................................... 3.2. Keyboard Optimization 38 3.2.1. Models ............................................................................................................. 38 3.2.2. Extra Design Concems ................................................................................... 39 3.2.3. Current Designs and Speed Predictions .......................................................... 40

3.3. The Optimal Soft Keyboard - OPTI ................................................................... 41 ............................................................................................. . 3.3.1 Design Rationale 41

3.3.2. The OPTI Design .......................................................................................... 43 Chapter 4 . Experiment 1 ................................................................................................... 44

4.1. Method ................................................................................................................. 44 . . 4.1.1. Participants ...................................................................................................... 44

4.1.2. Apparatus ........................................................................................................ 44 ............................................................................................................. 4.1.3. Design 48

4.1 .4 . Data Collection ............................................................................................... 49 4.1.5. Procedure ..................................................................................................... 50

4.2. Results and Discussion ........................................................................................ 52 4.2.1. Visual Search Time on a Soft Keyboard ......................................................... 52 4.2.2. Ski11 Transfer fiom Touch Typing to Stylus Tapping .................................... 53 4.2 .3 . Text Entry Speed ...................... ... . ..,.,. ...................................................... 54 4.2.4. Error Rates ............................... ... .... ..................................................... 57

...................................................................................................... 4.3. The Next Step 58 Chapter 5 . Experirnent 2 .............................. ..... .............................................................. 59

5.1. Method ................................................................................................................. 59 . . 5.1.1. Partmpants ..................................................................................................... 59 5.1.2. Apparatus ........................................................................................................ 59

............................................................................................................. 5.1.3. Design 61 .............................................................................................. . 5.1 .4 Data Collection 62

...................................................... . .......................... 5.1 .5 Procedure .... . ...... 63 5.2. Resuits and Discussion ........................ .. .......................................................... 65

5.2.1. Text Entry Speed - The Leaming Curve ........................................................ 65 .................................................................................................... 5.2.2. Error Rates 68

.................................................................................. 5.2.2.1 . Cumulative Errors 68 .......................................................................................... 5.2.2.2. Chunk Errors 70 .......................................................................................... 5.2.3. Use of Space Keys 72

...................................................................................................... Chapter 6 . Conclusion 76 Bibliography .................................................................................................................... 79

...................................................................................................................... Appendices -87 A . Single Letter Frequencies of Comrnon English .................................................. 87

.................................................. B . Digraphs Involving Ten Most Frequent Letters 88 ............................................................................... . C . General Description of V 1 0 90

D . Listing of phrases used in expenment one .......................................................... 93 E . Sample text used in the typing test in the experiments ....................................... 94 F . Listing of phrases used in experiment two .......................................................... 95

................................................................................ G . General description of V2.0 96 ........................................ H . Calculating the theoretical MT of a randorn keyboard 98

List of Figures

Figure 1 3Com's PamZPiZot (a) and Microsoft's PaZmPC @) ...................................................... 2 Figure 2 Nokia's 9000 Series Communicutor ............................................................................... 2 Figure 3 A hypothetical keypad (a) .............................................................................................. 7 Figure 4 A hypothetical keypad (b) .............................................................................................. 8 Figure 5 Single-stroke irnplementation of the Roman alphabet (a) Unisfrokes (b)

Graffiti ..................................................................................................................... -14 Figure 6 A Q WERTY layout .................................................................................................... 1 8 Figure 7 An ABC layout ............................................................................................................ - 1 8 Figure 8 JustType keyboard ........................................................................................................ 19

...................................................................... Figure 9 A Dvorak layout showing alphabet only 19

...................................................................... Figure 10 A Fitaly layout showing alphabet only 19 ........................................................ Figure 1 1 A telephone keypad with letters Q, Z and Space 20

Figure 12 The elusive crossover point ........................................................................................ 23 Figure 1 3 The reciprocal tapping task on two targets with width ( W) and

............................................................. amplitude (A) for establishing the Fitts' law 27 Figure 14 Performing a Fitts' law tapping task ........................................................................... 27

........................................................................ Figure 15 optimal angle model: expert behavior 31 ................................................................. Figure 16 An approximation of optimal angle mode1 1 . . .............................................................................................. Figure 1 7 Distnbution of digraphs 33

............................................... Figure 18 The OPTI Keyboard for text entry on mobile systems 43 ...................... .................................................................... Figure 19 Control panel of V 1 . 0 ... 46

......................................................................... Figure 20 A session with the large fixed Iayout 47 ............................................................... Figure 21 A session with the large randomized layout 47

......................................................................... Figure 22 A session with the small fixed layout 48 .............................................................. Figure 23 A session with the srna11 randomized layout 48

Figure 24 Text entry speed ........................................................................................................ -55 ................................................................................................. Figure 25 Control panel of V2.0 60

............................................................. Figure 26 A session with OPTI layout in Expenment 2 64 .................. .............................. Figure 27 A session with QWERTY layout in Experiment 2 .. 65

Figure 28 Performance over sessions . (a) Entry speed (b) Learning curves and th .................................................................................... extrapolations to 50 session 66

......................................................................... Figure 29 Mean cumulative error over sessions 69 Figure 30 Mean chunk error over sessions ................................................................................. 71 Figure 3 1 Use o f optimal spaces with O P n over sessions ..................................................... ....73

...................................................... Figure 32 Theoretical and actual optimal use of space keys 74 Figure 33 A five by six version of the optimal keyboard ........................................................... 78

............. .............................. Figure 34 A Keyboard without fixed letter-key assignments .......... 98

Chapter 1. Introduction

Since the inception of pen-based computing in the early 1990s technology in this area has

advanced steadily. As more technologies are available and more developers are involved in

research and development, both hardware and software have continually progressed towards

products with better performance and higher user acceptance.

1.1. Mobile Computing

Pen-based computing systems inciude personai digital assistants (PDAs), personal information

managers (PIMs), slates, and other pen-based and handheld products.

More important, however, is the shifi in attention from the limited idea of '"pen-based

computing" to the more general notion of "mobiie computing". The latter encompasses a vast

temtory, including not only PDAs, PIMs, slates, and other traditional pen-based products, but

also cellular phones, pagers, remote controls for consumer products. or future "convergent"

products for interfacing to computing, cable, telephone, consumer electronics, and intemet

systems.

Compared to systems just a few years ago, today's mobile systems have more computing

power and functionality. The devices are both lighter and more rugged, and they are

ergonornicaily designed. Above d l , hand writing recognition has improved tremendously in

recent years.

The development of pen-based systems is as diversified as desktop systems. Different

processors and operating systems are found in different products. Examples of products are

3Com's Palm Pilot (Figure 1 a), Sharp's SE-500, HP's ZOOM, Psion's Series 3c, MicrosofYs

PalmPC (Figure 1 b) and Nokia's 9000 Communicator (Figure 2). Examples of operating

systems are Palm OS, Magic Cap and Microsoft Windows CE.

(a) (b) Figure 1 3Comfs PamZPilot (a) and Microsoft's PalmPC (b)

Figure 2 Nokia's 9000 Series Communicator

1.2. Text Entry with Mobile Systems

The rapidly expanding market for mobile cornputing systems has elevaied to the fore a dificuit

challenge: the need to manually enter alphanumeric information.

For mobile systems, the full-sized QWERTY keyboard has either disappeared, or has shnink to

such a size that touch typing with ten fingers becomes impossible. In many situations with

mobile computers, users need to hold the computer with one hand or both hands. Here, the

main input channel is reduced fiom ten fingers to as few as one figer. The term "one fmger"

is used loosely here because the "finger" can sometirnes be replaced by a stylus or a pen. (It

can even be extended to eye-tracking input where the only input channel is the focal point).

Without a Ml-size physicai keyboard, these systems resort to altemate techniques for text

entry, such as handwriting recognition, tapping on a soft keyboard, or a miniature physical

keyboard that can no longer facilitate touch-typing. Interacting with the system using Pen-

based technology brought forth both a new technique and new challenges.

Indeed, one of the attractions of pen interfaces is the prospect of using familiar pen and paper

skills for interacting with computers (Frankish et al. 1995). Besides functioning as a mouse,

Pen input has two additional advantages: a) manipulation is directly under users' focus. and b)

the keyboard and mouse are converged into one input device. This is translated into ease of

annotating and drawing; and the possibility of writing text information into the computer.

Even by today's standards, pen input is still simpler to implement and is more effective than

other input means such as voice recognition or eye tracking, which are not of prirnary interest

in this research.

1.2.1. How Pen-Based Computers Work

Pen-enabled displays in most of today's mobile computea are formed with two layers: a

display layer that displays the information, and a digitizing layer which either overlays or

underlays the display layer. The digitizing layer detects the pen's action over it: position,

movement, pressure or other signals sent by the pen or sensed by the layer, such as angle of the

pen or the click of the side burton on the barre1 of the pen.

When two layers are perfectly aligned on top of each other, user perceives one display that

"reacts" to the pen input. The computer, however, has several jobs to complete behind the

scene.

It has to detect any action by the pen, process the signals and display results back to the user

with minimum Iag. It is up to the software or hardware to display any interactions between the

pen and the computer. In other words, "writing" on a computer's display does not necessarily

leave an "ink trail" on the screen: a difference fiom writing with a normal pen and paper. In

translating pen input into text information, a field called handwriting recognition emerged.

Pen input fares very well for simple painting, selecting and annotating. When it cornes to

entering texhial information into the computer with a Pen, two techniques are commonly used:

handwriting recognition or so fi keyboards. These are described in the following sections.

1.2.2. Handwriting Recognition

One clarification is due; namely, that handwriting recognition is not a synonyrn for pen-based

cornputing or mobile cornputing. Handw-riting recognition is ody a subset of these. However,

the poor performance of handwriting recognition in the early 1990s greatly hindered the

acceptance of pen-based systems.

With early systems, daims of unconstrained, cursive hand-written input did not meet the

expectations of a demanding user community. Today, handwriting recognition products are

much improved (Blickenstorfer, 1996) and the support for languages other than English is

emerging. The pen-based systems, particularly PDAs and PIMs, are much better received

today than in the early 1990s.

Handwriting recognition can be implemented at either the sohvare or firmware level.

Recognition "engines" come in many varieties. Recognition performance can be enhanced by

exploiting context, dictionaries, constraining the symbol set @y using "modes". for example),

custornizing for specific users or training. Detailed discussion of different schemes in

handw-riting recognition is found in Chapter 2.

1.2.3. Physical Keypads and Soft Keyboards

Besides using handwriting recognition, text information can be input into mobile systems

through a soft keyboard or a physical keyboard. Mobile systems that support handwriting

recognition generally include a soft keyboard as well.

1.23.1. Pfiysical and Soft Keypads

A keypad is usually a small set of keys, either physicdly built in or irnplemented as software.

A keypad can be used to enter text. There is no clear line between a keypad and a keyboard.

Generally the number of keys found on a keypad is less than on a keyboard. Multiple letters

and sometimes, the rnix of letters and nurnbers are assigned to a key on a keypad. One

question with a keypad therefore, is how to distinguish what is intended when a key is hit.

Several schemes can be used:

By entering extra information to explicitly indicate the desired letter. For example, when

each key is assigned with three letters, users can hit a key once to select the first letter.

twice for the second, and so forth. But the interval between the multiple hits for letter

selection m u t be clearly distinguishabie fkom the interval between different letters. This

makes it dificult for users with different motor skills to use the device. It also leaves less

room for errors. For example, to enter a word ' T A Y on a keypad as shown in Figure 3,

the following steps must be taken.

1. Quickly tap the key labeled "ABC" three times to select letter "C",

2. Brief pause until "C" is registered,

3. Tap once on the key labeled "ABC" to select letter "A",

4. Brief pause until "A" is registered,

5. Quickly tap on the key labeled "STU" two times to select latter "T",

6. Brief pause until "T" is registered.

Figure 3 A hypothetical keypad (a)

If the keys also have numbers assigned to them, like a telephone keypad (Figure 4), then users

can tap a key once to select the set of letters and then tap a numeric keys to indicate the

position of the desired letter in the set. The following steps are needed to enter "CAT':

1. tap on key " 1 " to select "ABC",

2. tap on key "3" to indicate Ietter C's position,

3. tap on key "1" to select "ABC",

4. tap on key "1" to indicate letter A's position,

5. tap on key "7" to select "STU",

6. tap on key "2" to indicate letter T's position.

Figure 4 A hypothetical keypad (b)

This scheme requires that users switch back and forth between identiQing a letter on a key and

identiQing the position of the letter by identifying a numenc key.

The advantage of this method is that the timing is not cntical and every letter is composed of

two taps. The user can relax and take time to enter the text without worrying about the

tirneout.

Both methods above are explicit. Substantial learning and numerous steps are required for

even a smaii text-entry task.

Another method uses disambiguation to ease the cognitive load fiom users. Users only

have to tap on keys that contain the desired letters. An algorithm in the background catches

the sequence, finds the most likely word for the sequence, and then proposes the word(s) to

the user for approval. This will rely heavily on the disambiguation algorithm for accuracy

and for reducing user fnistration. For example, to enter the word "CAT" on a hypothetical

keyboard (Figure 4), a user only has to tap "1 ", " 1 ", and "7". Disarnbiguation choices

include '%AT7, "BAT' and "ACT' and possibly others. Extra step(s) will be required to

select the word from the given list. If the htended word is not on the list of choices given7

the scheme has to allow for explicit construction of words such as "ABU" (the little

monkey in Disney's Aladdin). This method must be learned at an extra cost of time and

effort.

Using a keypad to enter the alphabet can reduce the physical space required (either a physical

keypad or screen space for a software keypad). The greatest problem is usability.

The reverse of the above, however, is much easier. Using a well-known name to code a

sequence of digits has been widely used. An example is a phone number 1-800-UOGUELPH

(Don't call, this number does not exist as this thesis is written).

1.2.3.2. Soft Keyboards

A soft keyboard is a keyboard image displayed on the system's screen. It is tapped on with a

Pen. Entering text with a soft keyboard is similar to selecting a button in a dialog box. No

speciai ski11 is required except the simple point-and-click mouse action. In the case of a pen, it

is a "point-and-tap" action.

There are several advantages in using soft keyboards:

1) There is no ambiguity as long as each key is assigned only one letter per mode. Different

letten c m be assigned to the same key (as with physicai keyboards) and can be accessed via a

mode shift. Becaw keys can be assigned different captions through software, clutîered

prktings sometimes found on physical keyboards can be avoided.

2) They do not have to be visible al1 the tirne, as opposed to a physical keyboard. Compared

with physically attached keyboards, soft keyboards do not add extra weight to the device.

They can be activated when needed and hidden when finished.

3) They are easier to implement and consume fewer system resources compared with

handwriting recognition.

4) They do not Wear and tear and are easily upgradable.

However, there are several disadvantages of the sofi keyboards:

1) They take screen real estate when activated. An economicai problem here is that. since the

soft keyboard will likely be used with one frnger or pen, we only need to access one letter at a

time. But a soft keyboard has to display at least a set of keys at the sarne time and, therefore, at

any time, most of these keys are not used.

2) They require a minimum key size to be operable. If the keys are too small. not only will

they be hard to recognize, but they will also be difficult to tap. Performance might suffer with

a smaller keyboard. Other factors may also affect the usability of a small soft keyboard, such

as screen resolution, ambient lighting, user's vision and user's hand-eye coordination.

10

3) They generally lack feedback, especially tactile feedback. Most pen-based display panels

are pressure sensitive. Ody a small pressure is required to activate a key. Most of the soft

keyboards rely on visud feedback - a visual "button-down" effect or reverse video - when a

key is tapped. As well, usen c m not sense the edge of a key with the fmger or pen. Finally.

eyes-free operation with a soft keyboard is not possible.

However, the advantages of soft keyboards hold the potential of supporting the easiest and

fastest way of entering text uifonnation into a mobile system, despite the drawbacks.

The most cornmoniy adopted layout for soft keyboard is QWERTY. The QWERTY layout

was designed to slow down typing speeds to reduce mechanical jam on typewriters (Potosnak,

1988). There is no obvious reason why it is the layout of choice for soft keyboards. We will

present a full discussion on this in the following chapten.

1.3. Focus of this Work

This research is focused on the issues in entering textural information into a cornputer system

using soft keyboards. These issues include the following:

Theoretical and empirical models related to soft keyboard design and usability;

Prediction of text entry rates with soft keyboards;

Exploring the problem of optimizing a keyboard layout for maximum entry rates;

Testing the usability of the optimal soft keyboard with text entry tasks.

Chapter 2. Literature Review

The notion of "text" here refers to either or both alphabetic and nurneric information. The

alphabetic information is language specific and, hereby, only refers to the English ianguage.

However, most of the models and rules will apply to other languages as well.

Advances in technology enables us today to have a choice of using either keyboards or

handwriting recognition to interact with cornputers. We also have a better understanding of

when and where handwriting recognition will be more appropriate than a keyboard.

2.1. Text Entry Using Recognition Software

To implement handwriting recognition, a system must fint be capable of accurately capturing

the x-y coordinate data of pen-tip movement. This development in the late 1950's precipitated

considerable activity in on-line handwriting recognition. This intense activity lasted through

the 1 9601s, ebbed in the 197û's, and was renewed in the 1980's (Tappert et al., 1990).

Generally, handwriting recognition cm be categorized as on-line and off-line recognition. The

distinction lies on two factors: when to capture the data and when to recognize the data.

On-line recognition usually captures data irnmediately and recognizes it as soon as the data are

captured. Off-line recognition captures the data immediately and fmishes with recognition

days, months, or even years later. It can also be that the data are captured at a later time.

(Optical character recognition or OCR, is a typical off-line recognition process).

12

An ideal on-line handwriting recognizer should match user input in both speed and accuracy.

It should not introduce any noticeable lag and should always work at user's pace. At the same

thne it should deliver satisfactory recognition accuracy. However, the ideal may never arrive:

one can always scribble something worse than a physician's prescription.

There are several ways to categorize handwriting recognition technology: some recognition

engines are character-based while others are word-based. Most begin recognition

instantaneously but some use deferred recognition. Some rely on printed characters whereas

others attempt to recognize cursive writing (Blickenstorfer, 1997). Many recognizers make no

effort to force users to provide manageable input but others have invented a set of symbols that

users m u t use to enter text (MacKenzie and Zhang, 1997).

2.1.1. Character-Based Recognition

Character-based recognition makes no effort to recognize words. It relies on the user to input

high quality writing. It also relies on constraining the possible characters in a small set. Mode

shifts are inevitable. For example, if user scnbbles a letter "C", it is dificult for a character-

based recognizer to know if it is in upper case or lower case without first being provided with

an additional constraint.

Another difficulty in implementing recognition algorithms for handwriting or hand printing is

known as the "segmentation problem". This occurs because some letters, or symbols, are

composed of multiple strokes. This c m also be thought of as the problem of "when-to-

recognize".

Several solutions are used for the "'when-to-recognize" problem. These include using a delay

tirneout, limiting the input in boxes or a comb-shaped entry line, or inventing a new stroke

alphabet in which each letter is created with a single stroke.

A single stroke, in this sense, is a continuous gesture of any shape created in one action. The

stroke begins when the pen touches the writing surface of the system and ends when the pen is

raised. This greatiy simplifies recognition because the segmentation problem is avoided.

Examples of single-stroke alphabets are Unistrokes (Goldberg and Richardson, 1993) invented

at Xerox PARC and Grami (EHickenstorfer, 1995) by Palm Cornputhg (Figure 5). Graflti

has been built in to several PDA's including 3Com PulmPilot and HP OmniGo 100. Another

commercial product Gesture Mosaic by Mosaic Input Technologies clairns to be "Virtually

100% accurate". "Each character has a unique single stroke for fast input" and can achieve text

entry speed of "up to or greater than 40 wpm" (Gesture Mosaic advertisement). No formal

usability studies have been reported on Unistrokes and Gesture Mosaic.

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Figure 5 Single-stroke implementation of the Roman alphabeti

(a) Unisrrokes (b) GraDti

' The black dot indicates the starting position for each stroke.

14

The major concern with handwriting recognition is recognition accuracy. Evaluation of

character-based recognizers is relatively easy. Each letter is either correctly recognized or not.

Evaluation of word-based recognizers can be more difficult depending on how accuracy is

evaluated.

A usability study of Grami showed that after only 5 minutes of learning (alphabet only) an

accuracy of 97% could be achieved. Resuits also showed that participants in the study

demonstrated complete ski11 retention on Graflti following a one-week lapse without further

practice (MacKenzie and Zhang, 1997).

Handwriting recognition usability is of concem not only for alphabetic entry, but also for

numenc entry. MacKenzie et al. (1994) tested four different methods for numeric entry.

Although the data were nurneric, entry speeds were calculated using the commonly accepted

five-character-per-word formula for text entry. Tapping on a soft keypad was the fastest (30

wpm) and most accurate (1.2% error rate), compared to hand printing (using a Microsoft's

handwriting recognition software included in the Microsoft Windows for Pen Computing 1.1)

which scored second on both speed (18.5 wpm) and accuracy (10.4% error rate). The other

two stroking methods, moving Pie Menu and Pie Pad received the lowest performance scores.

In a survey of 18 recognizers, eight developers quoted accuracy of 98-100% without

qualification. The others qualified claims with statements such as "writer dependent", "50% to

100% depending on application", "up to 100% based on training effects", or "100% if

characters written as prescribed" (Blickenstorfer, 1996). One reason it is difficult to quantifi

accuracy is that the hurnan element must be considered. As the survey noted, "our approach

15

was simply to ask what each developer claims the accuracy of their recognizer to be. In the

absence of a standard benchmark for recognition accuracy, this and our subjective expenence

with the products is d l we have to go on" (Blickenstorfer, 1996).

2.1.2. Word-Based Recognition

A word-based recognizer on the other hand, will fmt try to recognize each single character in a

word, make a match in its intemal library and come up with the closest suggestion for user

approval. This is in some sense similar to disambiguation as discussed earlier with keypad text

entry (section 1.2.3.1).

Handwriting recognition has advanced recently and today's handwriting recognition software is

much more satisfactory than products fiom the early 1990s. The 1st word on handwriting

recognition is that for a handwriting recognizer to achieve 100% accuracy is only illusionary

because even human beings are not able to recognize every handwritten text without any doubt

(Neisser and Weene, 1960). Hurnans are able to recognize about 96% of hand-printed single

characters, i.e., without contextual information (Frankish et al., 1995). There will aiways be an

obligation for the writer to write clearly. Generally recognition rates of 97% or higher are

acceptable by most w r s (LaLomia, 1994). Furthemore, there is always a tradeoff on speed

and accuracy with on-line recognizers.

2.2. Text Entry Using Soft Keyboards

In the world of desktop computing, the idea of using a non-QWERTY layout is a tired notion

that is of minor interest today. Even though altemate key layouts, such as Dvorak (Potosnak

1988), or chord keyboards (Noyes, 1983; Gopher et al., 1985; Gopher and Raij, 1988), exist

and some can support higher entry rates, substantial practice is required to become proficient in

their use. This, combined with the large installed base for QWERTY keyboards, has ensured

the continued role of QWERTY as the keyboard layout for desktop computing.

For soft keyboards, however, the arguments for using a QWERTY layout are diminished

because touch-typing ski11 will not necessarily transfer to "touch tapping". The motor ski11 of

two-handed eyes-fiee touch-typing is very different from the simple act of one-handed eyes-on

tapping with a stylus on a sofi keyboard. That is, if we compared two touch typists - one 25

wpm and one 75 wpm - in their ability to touch tap, it is not certain that a 75-wpm typist

would be façter, or substantially fàster, than would a 25-wpm typist.

Although it is not necessary to keep the antiquated, inefficient QWERTY layout, most people

still prefer a QWERTY layout for a soft keyboard. One reason is the familiarity with the

QWERTY layout on desktop keyboards. Another reason is that most designers saive to

duplicate a physical keyboard as closely as possible instead of optimizing a layout for the

screen (Blickenstorfer, 1997). Yet another reason is that most of the layout designs other than

QWERTY are either still at a research Ievel, or have not been tested for their usability and user

acceptance. Besides the keypads that have been introduced earlier, several commercial,

hypothetical and research-level layout designs will be discussed below.

17

MacKenzie et al. (1994) tested two soft keyboard layouts, QWERTY and ABC (a keyboard

with two rows of letters in alphabetical order and hence named), and found that Q WERTY was

faster (23 wpm) than ABC (1 3 wpm) but has a higher error rate (1.1 %) compared with that of

the ABC layout (0.6%).

Figure 6 A QWERTY layout

Figure 7 An ABC layout

Other research by MacKenzie, Soukoreff, and Zhang (1996) extended to six diflerent layouts.

In addition to the QWERTY and ABC layout, four other layouts were also tested. These

included a keypad style layout with disambiguation narned JzistType (Figure 8), a DVORAK

layout (Figure 9), a commercial design cailed FlTALY (Figure 10) and a telephone keypad

layout (Figure 11). On a telephone keypad, Ietters "Q", "Z", and Space are assigned to keys

"7", "9" and Y", respectively. This arrangement allowed w r s to enter the full English

alphabet. Disarnbiguation is also assurned for the telephone keypad layout.

Figure 8 Just Type ke yboard

Figure 9 A Dvorak layout showing alphabet oniy

Figure 10 A FitaZy layout showing alphabet oniy

Figure 1 1 A telephone keypad with letters Q, Z and Space

Again, QWERTY was found to be the fastest (21 wpm), followed by ABC (10.7 wpm). The

experiment, however, tested only the users' initial exposure to the layouts, except for

QWERTY. This is a major defkiency, because the experiment failed to show the potential of

the different layouts given sufficient practice.

Besides a fixed layout, researchers have also explored other possibilities. One of these was a

soft keyboard introduced as IBM/BellSouth's Simon - a combination of PDA and ce11 phone.

It was small and only displayed five or six lei-ters. If you picked one, the letters then changed

to the ones most likely to be used next in the English language (Blickenstorfer, 1997).

Although displaying a small set seemed to be a good idea in saving screen space, the usabiiity

of such keyboards must be tested. Factors such as visual search will add to the difficulty of

use.

Using a mode switch c m also reduce the size of the key set. This idea was borrowed from

physical keyboards where a Shift key switches between different assignments on the sarne

physical key. Mode switching c m be extended to different activities. This can also create

problems in usability. For example, with disambiguation-enabled keyboards or keypads, a user

must occasionally switch between tapping/entering text and selecting a word fiom a list of

choices. If the word is not in the list, the user hm to switch modes, such as "customize user

dic tionary ", "custornize List order", etc.

Another mode switch example is seen in some existing soi? keyboard designs. When a user

taps on the shiA key to switch between upper case and lower case, the key captions of the

entire keyboard are switched to reflect the new selection. The user is forced to switch between

searching among lower case and searching on a keyboard with al1 upper case captions. This

not only adds to difficulty in use but also to complexity in the software and lags due to

repainting the screen.

This is not a problem with physical keyboards for two reasons: (a) letters are al1 printed on the

key caps physically, and (b) most of the operations are eyes-fiee, especially with touch-typists.

Modes are necessary for reducing the size of a set. They add cognitive load upon users,

however. Unless necessary, over-use and over-expression of modes should be avoided.

It is obvious that any new technologies, even a new keyboard layout, must be leamed before a

user can be proficient. Two questions thus have &sen:

1. 1s it better than the existing technology? In other words, is it worth learning?

2. How difficult is it to learn, or how long will it take for an average user to become

proficient?

For soft keyboard designs, Soukoreff and MacKenzie (1995) presented a theoretical model for

the upper and lower bounds of typing speed using sofi keyboards. This was an attempt to

ariswer the fïrst question above. Using the model, MacKenzie el al. (1 997) predicted the lower

bounds and upper bounds of six different keyboard layouts using different text entry schemes.

The lower bound, or the enhy-level speed of a keyboard, represents the immediate usability of

a design. It predicts the performance of a novice user. The upper bound, on the other hand,

predicts the best performance an average user can possibly achieve. The two questions above.

therefore, can be re-addressed as how to establish the lower and upper bounds of a new

technology.

The theoretical and empirical models for calculating the predictions of lower and upper bond

speed of a sofi keyboard are discussed in detail in section 2.4.

While both the lower and upper bounds can be predicted, the lower bound is of less interest.

The upper bound represents the performance potential of a technology. The achievement of

that will be of primary interest in this thesis.

Whether a theoretical upper bound prediction is achievable and how long it will take before

users c m achieve this performance are two separate issues. The former will be determined by

the accuracy and sufficiency of models and the latter must be deterrnined empirically. In other

words, a specific learning mode1 based on experimentation must be established.

23. The Elusive "Crossover" Point 4

Ernpirical evaluation is the fmal "road test" of a new technology. In general when users are

involved, the new technology rnight start with lower performance than that of the current

technology. But because of the potentiai of new technology, the performance rnay catch up

and evennially exceed the performance of existing technology with prolonged use or training.

This is known as a crossover point of the two performance curves dong a time axis (Figure

12).

Crossover

b Time

Figure 12 The elusive crossover point

This crossover point rnay not be achievable in al1 cases. If, for example, it is not known

whether the new technology will eventually perform better than current technology, the "New"

curve might very well stay below the "Current" curve. A usability study might be terminated

before the two c w e s meet. In many cases, the problem is not knowing "when" the two curves

23

will cross at the planning stage of a usability study. This problem can be partially solved by

running a srna11 "pilot" study before the full user test to estirnate the position of the crossover

point.

McQueen et al. (1995) conducted a usability study on numeric entry using two entry methods

with a stylus. The crossover point of total entry time occurred at session 7 (20 minutes each)

of a total of 20 sessions.

Although with a prematurely terminated study, the crossover point can be reached by

mathematically extmpolating user performance data, the case will not be as convincing.

2.4. Models

As stated in Focus of this Work (section 1 . 3 , our goal was to design an optimal soft keyboard

that supports higher text entry speed than the 'status quo' Q WERTY layout. The models

descnbed below are al1 related to sofi keyboard design. and most of them will be used directly

in designing and predicting the upper text entry speed limit of an optimal keyboard.

2.4.1. The Linguistic Mode1 and Space Key Extension

In a paper by Maymer and Tresselt (1965) 20,000 words were sampled with a total of 87.201

letters. The work addressed many details on gathering a representative sample. Each letter

was examined for its frequency not only as a whole, but also for its different positions within

words.

Maymer and Tresselt's corpus provides a set of representative frequencies of alphabet usage in

"common English". These letter frequencies will be used in our prediction model.

Maymer and Tresselt's frequencies for letten in comrnon English, however, did not include

spaces. This was due to the linguist's focus on language. For text entry on cornputers,

however. a blank space is a character which must be inserted within text just as any other

character is inserted (Dix eî al., 1993). From the usen' perspective, however, this difference is

not visible. But for designing a soft keyboard, a space is the 27" character of the alphabet.

To modiQ the original character fiequency table, Soukoreff and MacKenzie (1995) added a

space for each word. The total nurnber of characters became 107,201. Dividing the total

number of characters by the total number of words in the sample, it results in about five letters

(including spaces) per word for the sample. This is usually considered the word size for

common English.

Another important piece of information in Maymer and Tresselt's work is the digraph

frequencies. A digraph is a pair of letters in a given order. "ON" is considered different fiom

"NO", for example. A digraph fiequency is the frequency of the digraph in the sample.

Again with the help of the letter positions in words provided by the original work. Soukoreff

and MacKenzie (1995) created a 27 x 27 digraph fiequency table.

2.4.2. Fitts' Law

Fitts (1954) described a mode1 for predicting the movement thne for rapid target selection

tasks. With a simple setup as illustmted in Figure 13 and Figure 14, Fitts found that the

movement time (MT, in seconds) is a fûnction of target width (ÇY) and amplitude (A). The

original Fitts formula is

MacKenzie (1989) found that the original formula bj

better fit of Fitts' experimental data:

LW = a + b x l o g , -+1 -(W 1

annon and Wea~ ler ( 1945) provided a

The logarithmic term of the formula is called the index of task difficulty ID, and carries the

unit "bits". Shannon's formula therefore, always gives a positive value (2 O) of ID with any

given A and W. The term 6 carries the units "seconds per bit". The reciprocal of b carries the

uni& "bits per second" or just "bps". This terrn is "throughput", as specified in ISO 9241 -92.

[SO 9241-9 is in cornmittee cirafi at the time of this writing.

26

Figure 13 The reciprocal tapping task on two targets with width (ÇY) and amplitude (A)

for establishing the Fitts' Iaw

*-.*/ Figure 14 Performing a Fitts' law tapping task

The study by MacKenzie et al. (1 99 1 ) measured the throughput value for pointing tasks using a

stylus as a cornputer's pointing device. The value was found to be 4.9 bits per second. The

intercept value (a) in the formula varies with experimental data, but is close to zero.

Theoretically it should be exactly zero, meaning that a task with zero index of difficulty would

take no time to complete.

Work by Epps (1986), Kerr and Langolf (1977), MacKenzie et al. (1991) and othen has

demonstrated that Fitts' law is applicable to the movement of a stylus as an input device. By

extension, Fitts' iaw is applicable when stylus movements are between 'keys' on a QWERTY

keyboard simulated on a pen-based computer's display (Soukoreff and MacKenzie, 1995).

MacKenziels formula will be used throughout this thesis work for calculating the movement

tirne of this type of tasks:

2.4.3. Visual Scan Time Mode1

Hick and Hyrnan's visual scan time prediction mode1 (Hick, 1952 and Hyman, 1953) has been

widely accepted to calculate the time (also called Reaction Time, RT, in seconds) required for

finding an item among N items:

RT = 0.2 x log, N (4)

where N is the total number of items to search fiom and the dope 0.2 has a unit of seconds/bit.

For example, by substituting N with 26 we get the visual scan time for searching a letter among

26 alphabets:

This model, however, is sensitive to the dope of the equation. In Welford's extensive review

of choice reaction time studies (Welford, 1968: 60- 104), slopes Vary fiom about 1 60 mshit to

about 320 rnslbit. The RT calculated with Welford's slopes therefore, range fiom 761 ms to

1.52 seconds. respectively.

The choice reaction time model describes the user behavior on an unfamiliar set of objects,

such as a new keyboard layout. With an expert user, total familiarity is assumed. Therefore,

for experts the dominating time required is to move fiom key to key without any searching.

Hick-Hyman's model, therefore, will o d y be used to predict the novice behavior, or. in other

words, the lower bound speed of a keyboard.

However. it should be noted that this does not mean the "slowest entry speed" as opposed to

the upper bound. which means the "fastest speed". The lower bound should be interpreted as

the entry-level performance of a typical novice user.

2.4.4. Saccadic Eye Movement

The nature of hurnan eye movement known as saccadic movement also plays a role. Simply

speaking, when a person reads or looks at a picture or even stares at a spot on the wall, his or

her eyes make multiple, rapid, jumpy movements constantly. These movements are called

saccades. A penon with normal vision usually makes about two saccades per second. Most of

these eye movements range in magnitude from four minutes of arc to 15 degrees (Bahill and

Stark, 1979). If the area is too small or the items are too close together, it will be difficult to

keep the eyes still (try holding a book at an arm length and count the number of lines of the

ISBN barcode). To scan a larger area, head movement will be involved and it will take longer

time and more muscles will be involved to cornplete the coverage.

2.4.5. Soukoreff and MacKenziels Optimal Angle Mode1

Soukoreff and MacKenzie ( 1995) described a model to predict the theoretical upper speed lirnit

by stylus tapping on keyboards. This model States that if there is a key that has width or height

longer than one unit then an expert user would approach that key at the angle that equals the

outgoing angle toward the next letter. This is depicted in Figure 15. The original mode1 was

used for a QWERTY keyboard where one space key is laid out at the bottorn of the keyboard

and is stretched across the entire length. But this mode1 c m be applied to any keyboard

designs where such situations &se.

Figure 15 optimal angle model: expert behavior

An approximation of the optimal angle mode1 (as explained in Figure 16) is to simpli@ the hit

points on the long key(s) only at some discrete points. For example, to hit a square key, this

mode1 would assume that expert user would only hit the middle point. For a 2-unit long key

the expert user would hit either the point 0.5 unit from lefi edge or the point 0.5 unit fiom the

right edge.

Key 3

Figure 16 An approximation of optimal angle model

This mode1 will greatly simpliQ the calculation while having minimal impact on calculation

accuracy. A detailed discussion is found in the next chapter where we present the design of the

optimal soft keyboard.

2.4.6. Key Repeat Time

Common English also has cases where double letters such as "tt" or "Ii" are used.

Theoreticaily, entering double letters requïres no laterai pen movement. Therefore this key

repeat time cannot be modeled by Fitts' law. For an expert user, there will be no search time

required either. With stylus tapping on a soft keyboard, only the key repeat time will account

for the text entry time. A simple empincal test (Soukoreff and MacKenzie, 1995) showed that

when tapping with a stylus on a sofi keyboard displayed on a Wacom tablet, the key repeat

time was 0.153 seconds. In the test users were asked to tap on a Wacom tablet for one minute

and the repeat tirne was averaged. In the real world, double letters only require two taps. The

speed would degrade appreciably toward the end of one minute of tapping. The repeat time

wouid be best represented by the first few seconds of tapping. Another brute-force test was

conducted with 5 subjects to confirm the previous empincal result. In the second test. the

subjects tapped on a soft keyboard 25 times before the average was taken. This short sequence

only took 3 to 4 seconds. Ten such sequences were collected for each subject. The result was

slightiy shorter key repeat time: about 0.127 seconds. However. the importance of this should

not be exaggerated as double tapping only concributes a small portion to text entry time (about

2% of total digraphs found in cornmon English. Figure 17).

Ch-Ch Ch4 pace S pace-C h Duble

Digraphs

Figure 17 Distribution of digraphs

We have described al1 the models required for designing and predicting the upper bound entry

speed for a soft keyboard. The next chapter will describe the design of an optimal soft

keyboard for pen-based text entry.

Chapter 3. The Design of Soft Keyboards

In this chapter we will address design issues for an optimal sofi keyboard. We begin with a

discussion of issues pertinent to novices and then discuss issues important for the design of an

optimal soft keyboard - one that can support the highest possible text entry rates for experts.

3.1. Issues Related to Soft Keyboard Design

3.1.1. Visual Scan: Fixed vs. Random

Visual scan time is a dorninating factor for novice performance. On physicai devices such as a

keyboard or a design on paper, a problem in testing visual scan time is how to maintain the

'hovice status" of the user. Since humans learn fiorn experience and observation, novice status

diminishes quickly once a user starts to use a keyboard. Each time the user searches for a key,

adjacent keys are observed and information about these keys is stored in memory (key captions

and positions). As use progresses, the user shifts fiom being a novice to being an intermediate

user and, finally, toward being an expert.

To validate Hick-Hyman's mode1 and rneasure visual scan time on a sofl keyboard, finding a

way to maintain the "novice status" becomes interesting.

To capture the true novice expenence, the user m u t remain at the novice level while

performance data are collected. The unfamiiiarity of key assignments can be achieved through

software by randomly reassigning letters to keys after each tap.

3.1.2. Ski11 Transfer from Previous Experience

One expectation is that people who can touch-type on a QWERTY keyboard might benefit

from the familiar layout even when the keyboard size is reduced to allow only one-finger input.

With touch typing, people do not think of the location of the keys. Instead, they translate text

directly to their fmgea using motor memory. Experimental psychologists cal1 this a "motor

program" (Heuer et al.. 1985). This is also known as "eyes fiee" operation on a keyboard.

Will a touch typist actualiy benefit fiom the familiar QWERTY layout in a one-finger tapping

task? This is a question that we sought to answer.

Being farniliar with a keyboard layout fiom previous experience may have effect on initiai

performance. Even partial familiarity can benefit the initial performance. For example. when

tapping on a QWERTY keyboard, knowing generaily which half of the keyboard the letter 'T"

is in can lead to a quick reduction of the search scope - from the full keyboard to half the

keyboard. This translates into shorter search time (refer to section 2.4.3).

When searching dominates the performance, a user is still in a novice or a near novice state.

As use progresses, visual search will become less necessary. Other barriers will become more

and more prominent in affecting the performance. Worst of al1 is the time it takes to move

from one key to the next. Whether this is just speculation or reality remains to be answered.

35

3.13, Sue: Large vs. Smaii

Mobile systems today are genuig smaller and smaller, for example, the 3Com P a l d i l o i is

only 8 x 12 x 2.5 cm. The liquid crystai display area is only 5.5 x 5.5 cm. Touch screen

interfaces are even found on wristwatches such as Casio's VDB-MI, where the bottom 113 of

the screen displays context sensitive menu buttons that a user can operate with a finger.

Implernenting a sofi keyboard on such a small display area requires careful design, not to

mention the physicai dificulty when operating such a keyboard with one's finger. An

interesting question arises: Does keyboard size (within lirnits) affect user performance?

Fitts' law States that as long as the ratio of M W remains constant, the movement tirne will be

the sarne (refer to section 2.4.2). This oniy holds within lirnits, however. In practice, other

factors can affect rnovement time. Different sizes of keyboards may require different muscle

groups that in turn have different throughputs (Balakrishnan and MacKenzie, 1997). Saccadic

eye movement (see section 2.4.4) may also play a role in working with different sizes of

keyboards.

3.1.4. Learning Mode1

"Practice makes perfect".

Leaming effects have been well studied over the years. The learning curve which gives entry

time as a function of amount of practice can be approximated as follows (Card et al., 1978, De

Jong, 1957):

where T! is time required to complete a certain task at the first time; TV is the predicted time for

the same task on the triai; N is the nurnber of triais; and a is an empincally detennined

constant.

3.1.5. Possibilities for Alternatives

To summarize, we have the following motivations to designing a soft keyboard with supenor

performance to Q WERTY:

a) QWERTY is not optimized for either touch typing or pen-based text input. If we can

establish that ski11 transfer fiom touch typing to one-fingered tapping is not significant, then we

can project that keeping the Q WERTY layout is not necessary.

b) For experts, the most important factor that dominates the speed of pen-based input with a

soft keyboard is the cave1 t h e arnong keys. If we can reduce the pen's travel distance fiom

key to key, we can increase text entry speed.

c) Soft keyboard optimization is language specific. We have learned a great deal about the

single letter and digraph fiequencies of cornmon English.

d) The theoretical and empincal models that have been discussed are suficient for keyboard

design. Although there might be other models related to keyboard performance, they are

considered trivial (e.g. the effect of order of letter arrangement on user cognitive load, see

more discussions in Section 3.3.1).

With the above tools in hand, we are in a position to design a soft keyboard that will perform

better than Q WERTY.

Once again it should be noted that a new keyboard design might have a lower performance

initiaily. If our theoreticai models are correct we should expect that performance will exceed

that of QWERTY, given sufficient practice.

We should also note that no matter how confident and optimistic we are about a design,

empiricai testing with users is essential. A learning model is the final tool for our design.

3.2. Keyboard Optimization

3.2.1. Modeis

Here we bnefly list the rnodels used to design our keyboard; that is, to calculate the theoretical

upper text entry speed and to empirically test the design. Details on these models can be

found in section 2.4.

Linguistic model: single letter and digraph frequencies, including the space character, for

cornrnon English. The five-characterdword definition denved from this work will be used

in the calculation.

Fitts' law: for predicting the movement time fiom key to key. The Shannon I MacKenzie

formula will be used (Equation 3 on p. 28). The throughput value for a stylus is 4.9

bits/sec. For long keys, the width is set to the "smaller of' the two dimensions (MacKenzie

& Buxton, l992), which equals the height (refer to Figure 16 on p. 3 1 ).

Optimal angle model, especially the approximation of the Optimal angle model.

Key repeat the. We will use the value obtained by Soukoreff and MacKenzie (1995) of

153 ms per tap.

Learning model: to predict and extrapolate behavior based on an empirical test.

3.2.2. Extra Design Concerns

This section presents extra design concerns. These concems are specific to the design of sofi

keyboards:

The keyboard should have no dead space. Dead space is the space between keys where no

action is designated. Dead space is not necessary and will only increase the key-to-key

movement time.

The entire shape of the keyboard should be rectangular to fil1 the shape of a typicai window

where the soft keyboard is displayed.

There is no constra.int on the shape of keys. For the sake of simplicity, we will make al1 the

keys rectangular. There is no limit on the size of keys either. A keyboard can have keys of

any size as long as the entire keyboard meets the two constraints above.

3.23. Current Designs and Speed Predictions

In short, the calculation of the upper bound entry rate is to sum al1 the digraph distances

weighted by the probability of each digraph. This will be the mean distance (norrnalized to

have the same units as the key height) a pen needs to travel for entering comrnon English.

By substituting A with the mean distance and W with the height of key (square key has both

width and height of one unit) in MacKenzie's formula (equation 3) the mean movement time is

calculated. The rnean movement time (secondcharacter) is then converted into the entry speed

(words/minute) using equation 7.

I X

60 (shin) Speed (wpm) = -

MT (s) 5 (chadword)

When the design contains keys with length longer than one unit, the calculation of movement

tirne is dealt separately. The approximation of the optimal angle model is used to calculate the

shortest path of each digraph depending on the characters that follow the long key.

Note that the approximation only calculates near-but-less-than optimal performance.

Therefore, the prediction slightly 'under estimates' the actual upper bound speed.

The prediction for double letters uses the key repeat time model (Section 2.4.6). Although key

repeat time is applied to al1 the double letter digraphs, not al1 appear in cornrnon English.

Those that are not in common English will have zero digraph probability and will not

contribute to the movement time calculation of the keyboard.

Using the models just descnbed, the upper limit of entry speed of a QWERTY sofi keyboard

(Figure 6, p. 18) was calculated to be 43.2 wpm (with a slightly sub-optimal approximation for

char-space-char movement). Table 1 lists the upper bound text entry speed predictions of

several commercial and experimental soft keyboards, including the designs that will be

discussed in the following sections.

Table 1 Upper Bound Entry Speed Predictions of Several Keyboards

Key board Speed (Vm) Description

Q WERTY 43 -2

Dvorak 38.7

Fitaiy 55.9

JustType 44.2

Physical keyboard layout

Physical keyboard layout

Soft keyboard. No disambiguation

Sofi keyboard. With Disarnbiguation

Phone pad 22.6 Using 2-key scheme, see sec. 1.2.3.1

OPTI 58.2 Soft keyboard. Designed at HCI Lab

5 x 6 OPTI 59.4 Soft keyboard. Designed at HCI Lab

Note: The 5 x 6 OPTI is presented in Addendum in Chapter 6.

3.3. The Optimal Soft Keyboard - OPTI

3.3.1. Design Rationale

Several steps are involved in designing a soft keyboard.

When examining the single letter fiequencies (Appendix A), the first five letters - space, E, T,

A and H - make up 50% of the cumulative frequencies. The next 11 letîers rnake up another

40% and the last 1 1 account for only 10Y0 of the total. The design started by considering only

the more fiequent letters.

The single-letter fiequencies simply indicate that some letters are more fiequently used than

others. They reveal nothing about relationships between letters. The second step was to

consider digraphs. First, we picked the top ten lettee (fiom E to L) fkom the single-letter

fiequency lin. For each letter we listed the top 10 digraphs starting and ending with the letter.

The result is listed in Appendix B. For example, the digraph " H E accounts for 3.155

occurrences whereas "EH" did not make the list. The former done makes it desirable to put

"H" and " E as close together as possible on a keyboard.

It makes sense to have the letter sequence "HE" to appear in the design radier than "EH. The

ordering may play some role with users' cognitive behavior: A sequence of "THE" appearing

on a keyboard would feel more naturai than a "HTE" or "EHT". It's speculated that even with

expert users, naturaily ordered sequences make it easier and more cornfortable to use.

However, this rationale did not have a strong influence on the design. We expected that with

expert users, familiarity dimuiishes the importance of ordering.

Another rationale was to keep the lettee of similar shapes separate from each other to avoid

confusion during a quick scan. Examples include letiers "C" and "G". "Q" and "O", " M and

"W', and "F" and "P". Again, this is not rigorously accounted for as it is felt to have only a

minor effect on an expert's behavior.

3.3.2. The OPTI Design

Assembling the keyboard takes some imagination. Whereas testing the design can be done

mathematically. The final design of a new keyboard is shown in Figure 1 8.

Caiculation of the upper bound tapping speed is done with a specially designed spreadsheet

that ailows quick visual inspection and modification of the design.

Figure 18 The OPTI Keyboard for text entry on mobile systems

The upper limit of entry speed of the OPTI soft keyboard was calculated to be 58.2 wpm (with

a similar sub-optimal approximation). This is about 35% higher than the predicted speed of

43.2 wpm given earlier for the Q WERTY layout (Section 3.2.3).

Chapter 4. Experiment 1

Experiment 1 was designed to gain a better understanding of following issues in sofi keyboard

design:

1. Will Hick-Hyrnan's search Ume mode1 apply to novice behavior with a soft keyboard?

2. To what extent does the ski11 of touch typing transfer to stylus tapping?

3. Will the size of the keyboard affect the usability of a sofi keyboard?

4.1. Method

4.1.1. Participants

Twelve students and staff fiom the University of Guelph were paid to participate in this

experiment. Six were male and six were female, al1 were nght handed. Some were students in

computer science while others were in other life science disciplines. Ali had expenence using

cornputers on a daily basis. They were recruited fiom those who participated in our previous

experiments or fiom those who responded to our posting to the departmental graduate students

and staff ernail lists. No screening was done on the participants.

4.1.2. Apparatus

The experiment software was developed with Borland C++ 4.0 and ObjectWindows Library

(OWL 2.0). The host system was a Packard Bell 486DX-50 PC m i n g Microsofi Windows

44

for Pen ComputingTM 1.1. A Wacom PL-IOOV tablet was atîached to the system. The Wacom

tablet is both an LCD display and a digitizer. It has a 12-inch B & W backlit LCD display

panel with 640 by 480 pixel resolution. Using the combination of the tablet and host computer

enabled the experiment to run without system lag and allowed user entry to also appear on a

regula. VGA monitor. A special stylus used on the tablet functions as a pointing device.

Instead of pressing and releasing a mouse button to 'click'. users tap-and-lift the stylus on the

tablet surface to complete the same action. The stylus and tablet use the standard WindowsM

mouse driver.

The software developed for the experiment 1 was simply code-named V1 .O. It displays two

different sized keyboards for the text entry task. Each size also has two different layouts. One

is the standard QWERTY layout, showing the alphabet only. Another layout generates

randomized key assignments each t h e a key is tapped. The keys on the large keyboard are 28

x 28 pixels each, or 10 x 10 mm measured on the screen. The keys on the srnail keyboard are

18 x 18 pixels each, or 6.4 x 6.4 mm on screen. The size and layout can be selected as

experiment conditions from the control panel of the software (Figure 19). Further description

of the expenment software are found in Appendix C.

I

l nput device - Soft keyboard

(f Stglus I tablet Large (28x28 pixel]

r Mouse r S maIl (1 8x1 8 pixel]

r Other P Key radomization

@ Practice r Run Experiment

/ Number of Sentences n

. - - -

Figure 19 Control panel of V 1 -0

The task implemented in software was to memorize a short phrase of text and enter it with each

of the soft keyboards (Figure 20, Figure 21, Figure 22 and Figure 23)). This approach

simulates a text creation task in that the user knows exactly what to enter. This is in contrast to

text copy tasks which require the user's focus of attention to continually switch between the

source text and the keyboard. Figure 20 shows a session with the large fixed layout. A sample

phrase is displayed on the first line and user input is on the second line, shown as unfinished.

Figure 21 shows a session with the randomized keyboard layout. Note that keys are

randomized when a key is tapped. The space key is not randomized. Figure 22 and Figure 23

show two sessions with the small keyboard.

The scale of keyboards in the figures is 1 : 1.

THE CAPITAL OF OUR NATION THE CAPITAL OF OUA

Figure 20 A session with the large fmed layout

NO EXCHANGE WITHOUT A BILL

NO EXCHANGE WA

Figure 2 1 A session with the large randomized layout

DASHIHG THROUGH THE SHOW DASHING THROUGH A

Figure 22 A session with the small fixed layout

INFORMATION SUPER HIGHWAY I N F O m T I O N SUPEA

Figure 23 A session with the srnail randomized layout

4.1.3. Design

The experiment was a 2 x 2 within-subjects factorial design. The factors and levels of the

experiment were as follows:

Size (small, large)

Layout { fixed, random)

There were four different combinations of layout and size. Each participant finished four

blocks, one for each condition. Each block contained 10 phrases (Appendix D). The order of

the conditions was balanced to reduce interactions between factors.

4.1.4. Data Collection

For each key a user tapped on the keyboard, the following information was collected in the

data files.

Given character;

Given character's position (ID on the keyboard);

User entered character;

User entered character's position;

Time elapsed between characters (in milliseconds);

Error. (1 if the user character was different From the given character; O if the user

character was correct.)

The first Ietter in each phrase was excluded from the timing measurement and from the

calculation of the correlation of the Ietter fkequencies in the test phrases with common English.

This is because it is irrelevant to take the entry tirne after the phrase is displayed until the

participant taps the first letter.

Ten short sample phrases were used in the experiment. For each block, ten phrases were

presented to the participants in randorn-without-replacement order. The sarnple texts set is

listed in Appendix D.

The correlation coefficient for the sample text phrases against common English calculated at

letter fiequency level, as just described, was .95 162. The implication of this high correlation is

that the sample text is highly representative of common Engiish.

4.1.5. Procedure

The task was to copy the sample phrases using each of the soft keyboards.

The Wacom tablet was positioned on a separate desktop away from the experimenter's host

machine. The ceiling lights were turned off to remove the glare on the LCD panel and a table

lamp was used to provide ambient lighting for the lab.

Prior to the fornial test, al1 participants were given a typing test. The program Typing Tutor N

on an Apple Macinfosh cornputer was used to collect the data. The text used for the typing test

was modified fiom a longer story to fit on one screen and to show alphabetic characters with

minimum punctuation (See Appendix E).

After the typing test, participants were given written instructions explaining the task and

device. They were specifically asked to focus on both speed and accuracy. They were also

asked to ignore rnistakes and carry on with the rest of the phrase when a mistake was made.

The purpose of the experiment was not explained.

They were then given the tablet and the stylus. The tablet was tilted off the desk to provide a

good viewing angle (about 15"). It was also adjusted to have appropnate contrast and

bnghtness.

Prior to completing the session, participants were given a practice session with one phrase for

each condition.

In the formal test session, one phrase was displayed at a time. With the fixed keyboard layout

the participants only needed to copy the text. The visual search time was presumed minimal

since the layout was fixed as per a QWERTY keyboard. With the randomized layout.

however, they had to visually search for the letter on the keyboard afier each letter was

entered. When an error was made, a "click" was played through speakers.

The typing test was given again at the end of the last session.

4.2. Results and Discussion

43.1. Visual Search Time on a Soft Keyboard

The movement time Ton a random keyboard has two components

where MTradom is the average movement time on a randomized keyboard and RT is the visual

search tirne.

Ideally if we know MTrDndO, then what remains is RT.

In fact, we observed that the movement between keys was confounded and movernent time

could not be modeled by Fitts' law. This was a disappointing result; however, it does provide

additional insight into novice behavior on sofi keyboards. During a visual search when using

the randomized keyboard layout, we observed participants to consistently lifi the pen after each

tap. They held it in mid-air so that the hand was not in the way, then they reached back to the

tablet when the next letter was found. Some participants simply swung the pen over the

keyboard to follow the focal movement of eyes and tapped down as soon as the next letter was

spotted. In either case, this is not a simple target selection. Therefore the totai movement time

has three components:

Where MTcom+ is the movement tirne for the confounded pen movement a described above.

As long as we do not have a mode1 for MT,o~pod we cannot predict the RT. However,

MTrdo,,, can be caiculated mathematically (Appendix H).

4.2.2. S M Transfer from Touch Typing to Stylus Tapping

The typing speeds of our participants ranged fkom 19 to 74 wpm. n i e tapping speed ranged

from 15.4 to 25.1 wpm for the large keyboard and from 13.1 to 26.0 wpm for the small

keyboard, respectively. The relationship between the typing speed and the tapping speed for

the four keyboards is shown in Table 2.

There was little correlation between touch typing speed and stylus tapping speed on the soft

keyboard - r = -416 for the large keyboard and r = 323 for the small keyboard, respectively.

The simple interpretation is that a fast typist does not necessady tap faster on a sofi keyboard

than does a slow typist. Typing ski11 was not significantly transferred to tapping on the soft

keyboard. Although farniliarity does help, these two tasks are fundarnentally different. both in

mental mode1 and in the motor skills involved. This is support for our belief that more

effective soft keyboard design is warranted.

Table 2 Typing and tapping speeds of the twelve participants

Participant T ~ P P ~ ( w m )

Fixed L Fixed S Rand L Rand S

Average entry speed 21.17 19.97 5.33 5 -52

Standard Deviation 14.77 2.89 3.79 0.93 0.92

Correlation Coeficient (r) O. 116 O. 523 O. 073 0.081

Note: Correlation Coefficients are with typing speed (2nd col.)

4.2.3. Text Entry Speed

The text entry speed in this experirnent refiects only about the first 15 to 20 minutes o f use.

Our focus in this experiment was not on prolonged user leaming and proficiency.

The andysis of variance of text entry speed (Table 3) showed that There was a significant main

effect of keyboard layout (F i ,~1 = 314.53, p < -0001). There was no effect for keyboard size

(FI,, 1 = 2.626, p > .05). There was a significant interaction eflect between keyboard layout and

size (FiVI 1 = 7.18, p < -05).

54

Table 3 ANOVA: Entry Speed for Experiment 1

Source df SM MS F P

Participant 11 150.700 13.700

Layout 1 2752.15 2752.15 3 14.53 .O00 1

Layout * Participant 11 96.249 8.750

S ize 1 3.131 3.13 1 2.626 -1334

Size * Participant I I 13.1 18 1.193

Layout * Size 1 5.727 5.727 7.182 .O2 14

Layout * Size * Participant 11 8.772 .797

The large fixed keyboard was slightly faster (21.17 wpm) than the small fixed keyboard (1 9.97

wpm). When the keyboard was randomized, however, the smaller keyboard showed a faster

entry speed (5 .j2 wpm) than the larger one (5.34 wpm) (Figure 24).

Large Small

QWERTY Randorn

Digraphs

Figure 24 Text entry speed

The shorter search time for the srnall keyboard, as reflected by the difference in visual field

width, might have played a role. The large keyboard is about 50% wider and taller. Recall

&om section 2.4.4, that the maximum saccade is about 15 degrees. The distance fiom

participants' eyes to the tablet was about 30 cm; and the width of the small keyboard was

measured at 6.3 cm. This results in a field width of about 12 degrees. Using the same

assumption with the width of the large keyboard rneasured at 9.5 cm, the field width was about

17 degrees. This means on the average, more saccadic eye movement might have involved in

searchuig with large keyboard.

Although we did not enforce a precise head position, this calculation suggests that more

saccadic movement might be necessary for large keyboards. In other words, participants could

not get the full vision of the entire keyboard without having to move their eyes more ofien on

the large keyboards. This extra eye movement may be one of the factors that increased the

average search time. In the worst case, participants may have to move their heads to complete

the search.

With the large keyboard, the users' hands tended to block a relatively large area of the

keyboard. Moving their hands away to regain the full view took longer as well. Al1 these

factors would contribute to the slower performance with large, randomized keyboard.

Participants were not instnicted to anchor any body part. They tended to anchor wrists on the

rim of the tablet when using fixed keyboards. With the random keyboard. they would anchor

their elbows on the desk.

4.2.4. Error Rates

The anaiysis of variance (Table 4) revealed that there was a significant difference in entry error

rates between the two keyboard layouts (F1.11 = 17.87, p < .005). The fixed layout had an error

rate of 2.96% and the random layout had an error rate of only 0.73%.

Table 4 ANOVA: Error Rates for Experiment 1

Source df SM MS F P

Participant 11 39.219 3.565

Layout 1 59.185 59.185 17.870 .O0 14

Layout * Participant 1 1 36.432 3.312

Size 1 6.149 6.149 3.415 .O9 16

Size * Participant 11 19.805 1.800

Layout * Size 1 6.149 6.1 49 5.527 .O384

Layout * Size * Participant I I 12.238 1.1 13

The difference in error rate for the random layout we attribute to participants being more

cautious about making erron with the random layout than with the fixed layout. The

confidence in being familiar with the fixed QWERTY layout caused participants to be more

relaxed on errors.

4.3. The Next Step

From this experiment we have gained an understanding of the fundamental issues in using a

soft keyboard. We now move on to the n e a experiment to Mly test the usability of our

optimal keyboard for pen-based text input.

Chapter 5. Experiment 2

The second experiment was a longitudinal design to explore learning effects with the O P n

keyboard. We also sought to qualitatively assess its usability and acceptance, and this requires

an evaluation wîth users. The effects of the factors described in the design of the OPTI can

o d y be established through an acnial usability test.

5.1. Method

5.1.1. Participants

Five University computer science students participated in the experiment. Four were male and

one was female. Al1 were right handed and used desktop cornputers on a regular basis. None

had regular expenence on using a pen-based computer. They were recruited from a pool of

subjects who participated in other non-related experiments. Since we are testing a keyboard

specifically designed for English, we picked oniy those whose first language was English. Al1

were well informed about the time cornmitment required for the experiment.

5.1.2. Apparatus

The expenment software was developed with Borland C++ 4.0 and ObjectWindows Library

(OWL 2.0). The host system was a Packard Bell 486DX-50 PC running Microsoft Windows

for Pen Computing 1.1. A Wacom PL-I OU V tablet was attached to the system. These were the

same as in Experirnent 1.

The software developed for the experiment hvo was code-named V2.0. V2.0 displays two

different keyboard layouts for a text enûy task. One is the standard QWERTY Iayout showing

.the alphabet only. The other is the "OPTI", as s h o w earlier in Figure 18. The control panel is

shown in Figure 26. Further descriptions of the experiment software are found in Appendix G.

The experiment was conducted in the HCI Lab at the University of Guelph's Computing and

Information Science Department. To minimize interference fiom any other source the lab was

completely booked for the experiment. The entire experiment took about four weeks including

Saturdays. A speciai web site was created for information updates and scheduling.

-Run Mode F Practiçe r Run Expehent

Input device

6 S tylui / tablet

r Mouse

Keyboard Layout - @ QWERTY

r OPTl

r Correlation 1 Block Size: 0 50

Figure 25 Control panel of V2.0

5.13. Design

The experirnent had 2 x 20 within-subjects factoriai design. The two factors were:

Keyboard layout (QWERTY. OPTI)

Session (20 sessions).

Each session was about 45 minutes long and was divided into two 20-22 minute periods. One

of the two layout conditions was assigned in each half-session period in aitemating order from

session to session. The order of the conditions was balanced between participants to reduce

interactions.

Each haif-session contained severai blocks of triais. The number of blocks for each half-

session penod was controlled such that as many blocks as possible were collected within the

allotted time. Therefore. in the early sessions, fewer blocks (5 to 6) were administered than in

later sessions (9 to 1 1). A 5 minute recess was allowed between the two half-sessions.

Each block contained 10 phrases. These 10 phrases were randomly selected from a source file

of 70 phrases. Phrases were not repeated within blocks but repeats were allowed from block to

block.

The sarnple phrase set was tested for its correlation against the common English. The result

was r = .9845 for single-letter correlation and r = .9418 for digraph correlation. The complete

list of the phrase set is shown in Appendix F.

Each participant was scheduled to cornpiete 20 sessions for the experirnent. Sessions were

scheduled Mondays through Saturdays. They were separated by at least two houn and no

more than two days. This was to sirnulate "regdar use" of the system while trying to avoid

fatigue and to fit participants' reguiar daily schedules.

This is a longitudinal experiment aiming at practicing participants toward expert performance

on the OPTi design. Data collection was designed so that as many details as possible about

user input were collected.

5.1.4. Data Collection

For each key a user tapped on the keyboard, the following information was collected in the

data files.

Given character;

Given character's position (ID on the keyboard);

User entered character;

User entered character's position. The four space keys were identified separately;

Time elapsed between characters (in milliseconds);

Error (1 if the user character was different from the given character; O if user character

was correct).

5.1.5. Procedure

Each participant was given a typing test prior to the fmt session. A program Typing Tutor N

on an Apple Macintosh computer ninning System 7 was used. The text used for typing test was

the same as for Experiment 1 (Appendix E).

Each participant was then given written instructions explaining the task and the goal of the

experiment. They were asked specificaily to aim for both entry speed and accuracy. The

instructions also stated that if they made more than IO% errors with a sentence (about 3

mistakes) they should slow down on the next sentence to increase accuracy.

As designed, the length of each half-session period was controlled with a tirner. Once started

the software was self-administered. The entire session was monitored on a separate CRT

connected to the system.

When participants had to skip a session and the next scheduled session was more than two

days, then the data fiom the upcoming session was not used. Instead, the participant was asked

to use that session as a 'warm-up' session and a new make up session was scheduled.

They were then given the tablet and the stylus. The tablet was tilted off the desk to provide a

good viewing angle (about 25'). It was also adjusted to have appropriate contrast and

brightness. The overhead lights were tumed off to reduce the glare on the tablet's display

panel. The height of the desk was 26 inches, a standard height for typing. The desktop can be

raised by about 2 inches to allow for different body sizes of participants.

The participants were asked to copy each short phrase by tapping on the sofi keyboard. A sofi

audio feedback "tick" was played to the users through speakers. When an error was made, a

louder "click", which was rnuch more prominent, was played. The participants were asked to

ignore errors and carry on with the next correct Ietter pointed at by the cursor (Figure 26

and Figure 27).

A typing test was given again when participants ffished the 20" session.

A plot chart was set up during the experiment to keep the participants motivated. Performance

expectations were not explained, however. Instead, they were constantly reminded to do their

best on both layouts.

1 NON PROFIT ORGAHI ZATION

NON PROFIT ORGANIA

Figure 26 A session with OPTI layout in Experiment 2

VIDE0 C-RA OITH ZOOM ïENS

VIDE0 CAMERA BITH A

Figure 27 A session with QWERTY layout in Experiment 2

5.2. Results and Discussion

5.2.1. Text Entry Speed - The Learning Curve

The most interesting and exciting result of this expenment was the learning curves (Figure 28).

The analysis of variance of text entry speed (Table 5) showed that there was no main effect for

keyboard (Fi,., = 0.60, p > .OS). There was a significant effect of session (F19.76 = 89.2. p <

-0001) and a significant keyboard by session interaction (F19,76 = 34.3, p < ,0001).

OPTI had a poor performance at the beginning, averaging 17 wpm, while QWERTY averaged

about 28 wpm. Through the extended use of OPTI, OPTI keyboard eventuaily out-performed

QWERTY significantly (Figure 28 a). The crossover occurred at the tenth session. This is just

under four hours of practice. As the experiment progressed OPTI exhibited better and better

65

performance in entry speed while QWERTY tended to level off. The t ea entry speed for OPTI

reached nearly 45 wpm by the 2oLh session and the performance of QWERTY reached about 40

wpm-

OPTl Q W E R N

- OPTI ------ QWERTY

Session

Figure 28 Performance over sessions. (a) Entry speed (b) Learning curves and

extrapolations to 50' session

Table 5 ANOVA: Entry Speed for Experiment 2

Source df SS MS F P

Participant 4 2465 -967 6 16.492

Key board 1 44.180 44.180 -600 -48 18

Keyboard * participant 4 294.476 73.619

Session 19 6204.993 326.579 89.205 .O00 1

Session * participant 76 278.235 3.66 1

Keyboard * session 19 935.522 49.23 8 34.3 15 .O00 1

Keyboard * session * part. 76 109.050 1.435

When we plotted two leaming cuves, the squared correlation coefficients were r' = 0.997 for

OPTI and r2 = 0.980 for QWERTY (see Section 3.1.4 for more about learning model). This

impiies that the fitted learning models provide a very good prediction of user behaviour. In

both cases over 98% of the variance is accounted for in the modek.

Considering that this longitudinal study lasted only 20 sessions. Most of the participants had

not become "experts" on the OPTI layout by merely ten hours of use. So, we mathematicaily

extended the learning curves for another thirty sessions to project their performance with an

even longer practice. The extrapolation of QWERTY performance stopped at 44.8 wprn and

OPTI would reach to 60.7 wpm. These two values are close matches with our theoretical

upper bound predictions: 43.2 wprn for Q WERTY and 58.4 wprn for OPTI (Figure 28 b).

5.2.2. Error Rates

An error was recorded when the user-entered character was different from the given character.

It couid occur under different circurnstances, and the technique of analyzing these errors can

affect the way we explain user behavior.

Figure 29 and Figure 30 show the error rates of the two keyboards over sessions. Two

different types of errors are discussed below.

5.2.2.1. Cumulative Errors

The cumulative errors were collected each time an entry error occurred. This is the "normal"

to measure errors. It provides a generic description of how accurate a device is in perfonning

given task. It does not attend to how an error occurred. The cumulative error rates ranged

fkom 2.07% for OPTI and 3.21% for QWERTY on the first session to 4.18% for OPTi and

4.84% for QwERTY on the 2 0 ~ session.

An analysis of variance (Table 6) revealed that there was a significant difference in cumulative

mors between the two keyboard designs (Fin = 12.294, p < .OS). QWERTY had consistently

higher error rates throughout the expenment. There was also a significant increase in

cumulative erron over sessions (F19,76 = 4.419, p <.OOl). This may have occurred because

entry speed increased over sessions, thus participants' input tended to continue into the reaction

time phase of error. It was noted that participants wanted to challenge themselves on entry

speed and tended to be more relaxed on errors. They were instmcted to focus on both speed

and accuracy (section 5.1.5) and were reminded of this when errors got too high. However, it

seems that cumulative error may not actuaily descnbe participants' behavior during the

experiment.

+ Opti r,. Qwerty

1.0 .

0.0 - -- - - - - - - - - - - - - . -

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sessions

Figure 29 Mean cumulative error over sessions

Table 6 ANOVA: Cumulative Erroe for Experirnent 2

Source df SS MS F P

Part. 4 122.723 30.68 1

Ke yboard 1 39.761 39.76 1 1 2.294 ,0248

Keyboard * participant 4 12.937 3 -234

Session 19 1 15.520 6.080 4.3 19 .O00 1

Session * participant 76 104.574 1.376

Keyboard * session 19 10.148 .534 1.582 -0830

Keyboard * session * partic. 76 25.657 -338

5.2.2.2. Chunk Errors

The experiment was designed to control the error by forcing the user to always keep

synchronization with the given text. As the entry speed increased, another type of error may

have occurred more ofien. This has been called a chunk error (Matias, MacKenzie and

Buxton, 1996). This occurs, for example, when a user is unsynchronized with the given text as

follows :

Al1 the entries starting fiom the second space d e r "THE" (as s h o w underlined) would be

counted as errors. Thus eight (8) errors will be recorded. To correct for this, the user must

ignore "A" and enter "S". as indicated by the cursor +'", to regain synchronization.

In the above exarnple, it would be unfair to ascribe eight errors to the user, simply because the

words were shifted by one place. To compensate for this. we count a series of more than one

(inclusive) continuous error as only one error. We cal1 this a chunk error.

0.0 ---- - - - - - . - - - - - - - - - . - . - - - .

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sessions

Figure 30 Mean chu& error over sessions

Since a chunk error is a subset of a cumulative error, the chunk error rate must be lower than

the cumulative error rate. An anaiysis of variance (Table 7) showed that chunk error rates

increased significantly over sessions on both keyboards (Fi.4 = 16.695, p < .05). Chunk error

rates were d s o significantly lower for OPTI than for QWERTY = 4.809, p < -0001).

Matias et al. (1996) found that chun. errors did not increase as much as the cumulative errors

when the total entry speed increased. This suggested that the size of the chunks got longer and

longer since it was more and more diEcult for user to react to errors when typing speed

increased. This was not the case in this study, and we attribute this to the different motor skiils

required. Matias' experiment tested touch typing with five fingers. The interaction between

fingers can cause a " d m roll" effect, which makes it difficult to stop when an error occurs. In

our experiment, however, such interaction did not exist because there was only one channel of

input at a time. Recall that a "chunk" has length of one for one-or-more concurrent error

characters. The consistent increase in chunk error rates with cumulative error rates suggests

that chunks did not get longer as entry speed increased.

Table 7 ANOVA: Chunk Errors for Experiment 2

Source df SS MS F P -- -

Participant 4 73 -280 18.320

Keyboard. 1 3 7.424 3 7.424 16.695 .O 150

Keyboard * participant 4 8.967 2.242

Session 19 64.986 3 -420 4.809 .O00 1

Session * participant 76 54.055 -71 1

Keyboard * session 19 4.260 -224 1.398 A538

Keyboard * session * partic. 76 12.189 .160

5.2-3. Use of Space Keys

nie OPTI iayout had four space keys (Figure 18, p. 43). For any character-space-character

sequence at Least one space key would create the shortest path, which is therefore referred to as

the optimal space key. The experiment was designed so that the use of different space keys

was transparent to users, but the data file would distinguish them as different keys.

In the experiment? participants were instructed that they could use any of the space keys as they

entering text. As the sessions progress, one would expect three major scenarios: (a) users

would attempt to use the optimal space key; (b) users would always use the closest space key,

following the first character of the character-space-character sequence or (c) users would tend

to use a "favorite" space key.

A favorite space is a personal choice. It does not necessarily have to be optimal. It rnight be

the one that stays visible more often than others, for example. We did not dwell on how users

chose or used space keys because they were only a small part of the keyboard. Figure 31

shows that the use of optimal space keys increased fiom 38% to 47%. Considering that for

each space entered there was one optimal space key4 and three non-optimal space keys, we c m

see that the optimal space did get more use than non-optimal space keys.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Session

Figure 3 1 Use of optimal spaces with OPTI over sessions

Having four space keys is convenient. But forcing users to always use the optimal space key

requires extra judgement on the fly and this is not likely to occur.

'' When the character-space-charmer pattern is symrnetrical there wouid be two or more space keys that create the same shortest path. In an extreme case with E-Space-E, ail the four space keys are equally optimal.

The four space keys were not created equal. Figure 32 shows the theoretical optimal percentage

of the four space keys and the actual use of these four keys. Note that the position of space

keys is Space 1 (top left), Space 2 (top right), Space 3 (bottom lefi), and Space 4 (bottom right)

(See Figure 18 on p. 43). Space 1 holds the highest share of being optimal but it was the least

fiequently used, causing a failback in overail performance.

O 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Session

Figure 32 Theoretical and actual optimal use of space keys

Recall that the ratio of the movement tirne of character-only digraphs to digraphs involving

spaces is 62:38. This means that 62% of the time, the pen travels fiom character to character

when entering common English. Table 8 lists the character and space time distributions of

cornrnon English across several keyboard designs. The mean MT gives the overall upper

bound speed limit. The M T C h - C ~ and iMTchhSpch are the percentage t h e required for movement

74

involving characters only and for movement involving both space and characten. From the

table we can see that in al1 cases space keys are doing very well in reducing the total MT: 38%

of the entry task takes only about 10% of the time, whereas over 60% of the entry task would

take 90% of the time for digraphs involving only characters. In other words, the designs are

biased in favour of the space key(s).

An ided distribution of the time would be one that matches the digraph ratio (62:38) as closely

as possible. Here we see a great potential of designing an even faster keyboard if MTCh-Chr and

not A47',ch-qKh, can be reduced.

Table 8 Movement Time Distributions

Common English Y

Digraphs Digraph QWERTY OPTI 5 x 6 OPTI

Fitaly Distribution

Mean MT (s) 0.278 0.206 0.202 0.21 1

Char - char 60% 89.6% 90.0% 89.0% 9 1 .O%

Double letters 3%

Char - sp & sp - char 38% 10.4% 10.0% 1 1 .O% 9.0%

Note: the 5 x 6 OPTI design is presented in the addendurn on p. 77.

Although the design was not handedness specific, later analysis showed that space 1 is most

optimal, followed by space 2 (refer to Figure 18 on p. 43 for key assignments and Figure 32 for

the distribution). This might inform the future design of soft keyboard as well.

Chapter 6. Conclusion

In this work, we have explored rnany issues in text entry using sofi keyboards and a stylus.

These include:

The effect of keyboard size;

The visual search time for novice users;

The theoreticai expert behavior in using a soft keyboard;

The relationship between touch typing ski11 and use of a soft keyboard;

Major issues in developing an optimal keyboard for stylus tapping.

A longitudinal study of user performance with soi? keyboards.

Predictions for the theoreticai upper limit for severd keyboard layouts;

The design of a new optimal sofi keyboard for pen-based text entry with a predicted upper

speed limit of 58.4 wprn.

We have the following major findings:

8 There was no significant effects for keyboard size on performing text entry within the size

range we tested;

The difference between our finding and Hick-Hyman's weil-studied formula on visual

search time RT can be attributed to a user behavior not modeled, namely, hand traveling

patterns during visual search.

76

There was a Iow correlation between touch typing speed and tapping speed. The low

correlation can be attributed to the different motor act, and different mental models in

transferrhg text entry tasks into key selection.

The empiricai results strongly supported our keyboard design and prediction. Usen were

able to perform better with the OPTI keyboard design over the traditionai QWERTY layout

after 4 hours of practice. A mode1 extrapolation suggested that the theoretical upper bound

b i t codd be reached if about 50 sessions (1 7 hours) of practice were received.

Data have shown that the OP- design is not only faster but also significantly more

accurate than a OWERTY sofi kevboard.

Addendum:

After we have concluded our experimentation on soft keyboard, we continued to explore other

design possibilities. One design that yielded even faster predictions than OPTI was a 5 x 6

design. It has a slightly better the-proportion between kfT'Ch-Ch and MTCh-spch. The theoretical

upper speed limit is 59.4 wpm (Figure 33). We also believe that an even faster keyboard is

possible.

Figure 33 A five by six version of the optimal keyboard

Since we now believe that spaces should be given less credit in text entry with soft keyboards,

perhaps a user custornizable "favorite space" would be a better choice. A favorite space key

could be one that is not in the way of alphabetic entry most of the time. Hitting a space could

be like casually visiting a neutral zone with minimum attention required. An example is to

have the entire rest of the window, or even the entire screen, as a space key.

A favorite space key could also be the one that is always the closest and easiest to hit. This

would always require the shortest movement time. An example would be a large space in the

center of the keyboard.

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Appendices

A. Single Letter Frequencies of Common ~n~l ish' .

5 From Maymer and Tresselt, 1965.

Single Letter count Space 1 19998

Cumulative fieq. % 1 8.66

B. Digraphs Involving Ten Most Frequent ~et ters~ .

The order of ten ietters are: E, T, A, H, O, S, N, R, 1, and L

From Soukoreff and MacKenzie, 1995. 7 DC = Digraph count. A "-" represents "Space". For example, HE occwed 3 155 tirnes. E-Space occurred 4904 tirne, etc. A blank indicates that there is no such digraph in the sample.

B. (continued)

C. General Description of V1.O

The experiment software generates two different sizes of soft keyboards each with two layouts:

fixed QWERTY or randornized. When key randomization is enabled, the key assignment is

completely randornized each time a key is tapped. Since the space key has a different shape

than the others, it is not reassigned with randomization.

The key dimensions of the large keyboard is 10 x 10 mm measured on the Wacom PL-IOOV

12" digitizing tablet screen. The small keys were 6.4 x 6.4 mm. The large keys have the same

height, in pixels, as buttons found in a typical MS Windows application's dialog box. The

small keyboard has nearly the same size as the on-screen keyboard found on a 3Comts

PaIrnPiIot.

V1 .O also allows for different input devices. When a stylus is selected the prograrn hides the

mouse cursor. Other device selections will enable the mouse cursor. It also has different

modes for practicing or for ninning experiment. In the latter case, al1 data are saved to files.

The original and participant-entered text are displayed in a 16 point bold uppercase mono-

spaced Courier font to help keep participants synchronized during text entry.

Once the first character is entered, the program collects a senes of data values:

The time elapsed since the last character was entered,

The given character at the cursor position (a caret """),

90

The position of the given character on the keyboard (The positions are numbered fiom

O for the top-left key to 25 for the bottom right key of the third row. Space is numbered

26.1,

The character entered by the participant,

The position of the participant-entered character and

Emr statu, (O = correct character, 1 = incorrect character).

An audible "tick" signal indicates an error.

The program automaticaily stops at the end of the phrase and displays a summary of text entry

speed, average elapsed tirne between characters, and error rate;

Utilities 1

Help - i

I

Options I

Data file namingl

Device 1 Sample set size 1

1 Load sample phrase set

I 1

Correlation test

Practice 1 Experirnent

I I I

I File information

I Subject ID

Session ID

Block ID

Setup keyboard

D. Listing of phrases used in experiment one

ALL GOOD BOYS DESERVE FUDGE

THE BACK YARD OF OUR HOUSE

MARY HAD A LITTLE LAMB

PROTECT OUR ENVIRONMENT

VIDE0 CAMERA WITH ZOOM LENS

LIFE IS BUT A DREAM

THE QUICK BROWN FOX JUMPED

BLACK AND WHITE IMAGE

CLOSED FOR THE SEASON

MOM MADE HER A TURTLENECK

E. Sample text used in the typïng test in the experiments

A queen asked her advisor, the grand vizier, why she never seemed able to fmd mental

contentment. The vizier indicated that her mind is constantly occupied with exquisite

possessions. When one's mind is absorbed with thoughts of jewels, palaces, and gold, it cannot

fuid equilibrium. The queen pondered the vizier's words, and realized that he had spoken the

tmth.

Listing of phrases used in experiment two

MOM MADE HER A TURTLENECK GOLDILOCKS AND THE THREE BEARS WE DID SOME GROCERY SHOPPING THE ASSIGNMENT IS DUE TODAY WHAT YOU SEE IS WHAT YOU GET FOR YOUR INFORMATION ONLY A QUARTER OF A CENTURY THE STORE WILL CLOSE AT TEN HEAD SHOULDERS KNEES AND TOES VANILLA FLAVORED ICECREAM FREQUENTLY ASKED QUESTIONS ROUND ROBIN SCHEDULMG INFORMATION SUPER HIGHWAY MY FAVORITE WEB BROWSER THE LASER PRINTER IS JAMMED ALL GOOD BOYS DESERVE FUDGE TKE SECOND LARGEST COUNTRY CALL FOR MORE DETAILS SHE ARRIVED N S T iN TIME HAVE A GOOD WEEKEND VIDE0 CAMERA WITH ZOOM LENS MONKEY SEE MONKEY DO THAT IS VERY UNFORTUNATE THE BACK YARD OF OUR HOUSE THIS IS A VERY GOOD IDEA THE MARCH READiNG WEEK OUR FAX NUMBER HAS C W G E D THANK YOU FOR THE HELP NO EXCHANGE WITHOUT A BILL THE EARLY BIRD GETS THE WORM BUCKLE UP FOR SAFETY THIS IS TOO MUCH TO HANDLE PROTECT OUR ENVIRONMENT THE GROWMG WORLD POPULATION

35. THE LIBRARY IS CLOSED TODAY

36. MARY HAD A LITTLE LAMB 37. TEACHING SUPPORT SERVICES 38. WE WILL GO SWIMMING TODAY 39. WE ACCEPT PERSONAL CHEQUES 40. NON PROFIT ORGANIZATION 4 1. USER FRIENDLY NTERFACE 42. EAT HEALTHY FOOD EVERY DAY 43. GET SOME HANDS ON EXPERIENCE 44. THIS WATCH IS TOO EXPENSIVE 45. UNITED STATES POSTAL SERVICE 46. COMMUNICATE THROUGH EMAIL 47. THE CAPITAL OF OUR NATION 48. TRAVEL AT THE SPEED OF LIGHT 49. 1 DO NOT FULLY AGREE WITH YOU 50. GAS BILLS ARE SENT OUT MONTHLY 5 1. EARTH QUAKES ARE PREDICTABLE 52. LIFE IS BUT A DREAM 53. TAKE IT TO THE RECYCLING DEPOT 54. SEND THIS BY REGISTERED MAIL 55. FALL IS MY FAVORITE SEASON 56. A FOX IS A VERY SMART ANIMAL 57. THE KIDS ARE VERY EXCITED 58. THIS PARKING LOT IS FULL 59. MY BIKE HAS A FLAT TIRE 60. DO NOT WALK TOO QUICKLY 6 1. DUCKS GO QUACK WHEN HUNGRY 62. IT HAS A LIMITED WARRANTY 63. THE FOUR SEASONS OF THE YEAR 64. THE SUN RISES IN THE EAST 65. IT WAS VERY W D Y TODAY 66. DO NOT WORRY ABOUT THIS 67. DASHING THROUGH THE SNOW 68. WANT TO JOiN US FOR LUNCH 69. STAY AWAY FROM STRANGERS 70. ACCOMPANIED BY AN ADULT

G. General description of V2.O

V2.0 is designed for ninning Iongitudinaily with a variable number of blocks per session. It is

designed to be self-contained. In each session, the expenmenter oniy needs to enter user id,

session id and the size of the block and select mode. The rest of the process is then under

participant's control without intervention by the experimenter.

It generates two different sessions with either OPTI or QWERTY layout. The data collected in

files are similar to that of V1 .O. It was tested extensively prior to the experiment.

I Start program menu I

Control panel - I

1 1

Mode

Load sample phrase set fiom file

Data file naming / I Layout

1

Automatic reset mouse cursor

4 Correlation coefficient

Help

1 Set experiment conditions I I 4 I Subject ID

Starting block ID

v v I Setup keyboard

4

Program structure of V2.0.

Session ID

& Calculating the theoretical MT of a random keyboard

MTmdo, is the mean movement t h e on a keyboard with key distributed in a fixed physicai

position without fixed letter-key assignments (Figure 34).

Figure 34 A Keyboard without fixed Ietter-key assignments

When we type on this keyboard, every tirne we hit a key, the next letter can be in any position

on the keyboard. Therefore the mean movernent time on this keyboard can be calculated

mathernaticall y without information about any speci fic language.

If the space is the only known key we can mode1 the movement time of such keyboard as

foilows.

Number the character keys from 1 to 26. The mean distance MDch=h br character to character

is:

And the mean distance fiom character to space (nurnbered as key 27) to character M&-yr,,:

Where DI- , is the distance between key 1 and key 1 (here is zero), Dl-r is the distance between

key 1 and key 2 and is the distance between key 1 and key 27, and so forth.

Therefore, the total movement time can be modeled by Fitts' law given that the mean distance

MD = MD,-J,~~ + MDch;.pi:

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