Expert Chording Text Entry on the Twiddler One–Handed Keyboard
Kent Lyons, Daniel Plaisted, Thad Starner
College of Computing and GVU Center
Georgia Institute of Technology
Atlanta, GA 30332-0280 USA{kent, plaisted, thad}@cc.gatech.edu
Abstract
Previously we demonstrated that after 400 minutes ofpractice, ten novices averaged over 26 words per minute(wpm) for text entry on the Twiddler one–handed chording keyboard, outperforming the multi–tap mobile text entry standard. Here we present a study that examines expert
chording performance. Our five participants achieved anaverage rate of 47 wpm after approximately 25 hours ofpractice in varying conditions. One subject achieved a rateof 67 wpm, equivalent to the typing rate of the last author
who has been a Twiddler user for ten years. We analyze
the effects of learning on various aspects of chording, provide evidence that lack of visual feedback does not hinder
expert typing speed and examine the potential use of multi–
character chords (MCCs) to increase text entry speed.

1 Introduction
Mobile computing is becoming one of the most widelyadopted computing technologies. There are currently 1.3
billion mobile phone subscribers and could be as many as 2
billion by 2007 [1]. Wireless text messaging is widespreadwith predictions of a rate of over 1 trillion messages per
year being reached shortly [4, 11]; unfortunately, slow text
entry on mobile devices may limit the utility of upcoming services such as wireless e-mail. In this paper, we
present an evaluation of a chording method of text entry on
the Twiddler, a 3x4 button keypad that offers rapid typing
rates. We explore the rates of learning for chording, present
data on our expert participants using multi-character chords(MCCs), and examine the effects of varying visual feedback
on expert typing speeds.

1.1 Twiddler Chording
Many wearable computer users [5, 14] type with the
HandyKey Twiddler (Figure 1), a mobile one–handed
chording keyboard with a keypad similar to a mobile phone.
The Twiddler has twelve keys arranged in a grid with three

Figure 1. Chord for the letter ‘j’ (R0L0) on the Twiddler

columns and four rows on the front. The device is heldwith the keypad facing away from the user and each row
of keys is operated by one of the user’s four fingers. Additionally, the Twiddler has several modifier buttons such as‘Alt’, ‘Shift’, ‘Control’, etc. on the top–back operated by
the user’s thumb. Instead of only pressing keys in sequence
to produce a character as with traditional keyboards, multiple keys can be pressed simultaneously to generate a chord.
The default keymap for the Twiddler is shown in Table 1
and consists of single button and two button chords which
are assigned in an alphabetical order. The four charactersin the Buttons column denote what keys to press from eachrow. ‘L’ indicates the leftmost button in a row, ‘M’ the middle and ‘R’ the right button. A ‘0’ means the corresponding
finger is not used in the chord. The chord for ‘a’ is ‘L000’
which indicates that the user should press the left button on
the top row. To generate ‘j’ (‘R0L0’), the user would press
the right key on the top row and the left key on the third rowButtons
L000
0L00
00L0
000L
M000
0M00
00M0
000M
R000
0R00
00R0
000R

Char
a
b
c
d
e
f
g
h
Space
DeleteBackspaceEnter

Buttons

Char

Buttons

Char

RL00
R0L0
R00L

i
j
k

ML00
M0L0
M00L

r
s
t

RM00
R0M0
R00M

l
m
n

MM00
M0M0
M00M

u
v
w

RR00
R0R0
R00R

o
p
q

MR00
M0R0
M00R

x
y
z

Table 1. Keymap for chording on the Twiddler.

With traditional keyboards, a character is generated
when the corresponding button is pressed. This strategy
cannot be used for chording since the user may not press allof the keys for the chord at exactly the same time. Instead,the Twiddler generates the keycode once the first button ofa chord is released. Just before this point, all of the buttons
for the chord have been depressed so the proper keycode
can be generated. In Section 2.2, we explore the relationship between the timings of pressing the buttons and how
they relate to learning to chord.
For a chord on the Twiddler, each of the fingers may bein one of four states (pressing one of three buttons, or notpressing anything). Ignoring the “chord” in which no buttons are pressed, there are then 44 −1 = 255 possible chords
using the four main fingers. The modifier buttons operated
by the thumb allow more chords. HandyKey includes what
we have termed multi–character chords (MCCs) in the default keymap: single chords that generate a sequence of several characters. For instance, there are chords for some frequent words and letter combinations such as “and”, “the”,
and “ing”. Users can also define their own MCCs. We
present an evaluation and analysis of the effects of MCCs
on expert typing rates in Section 3.1.

1.2 Previous WorkIn our previous work, we evaluated the relative learning
rates of typing with multi–tap versus typing with chording
on the Twiddler [6]. We conducted a longitudinal study withten participants. None of the participants had any experience with typing chords on the Twiddler. However, they
had varying levels of practice typing with multi–tap.
The experiment was a 2 x 20 within–subjects factorial
design in which we presented the participants with two con-ditions (multi–tap and chording) during 20 sessions of typing. A session consists of two parts delineated by typing
condition and a five minute break in the middle. Each partof the session, which lasts 20 minutes, consists of several
blocks of trials. A block contains ten text phrases of approximately 28 characters each which were selected randomly from the set of 500 phrases developed by MacKenzie and Soukoreff [9]. These are phrases specifically designed as representative samples of the English language.
The phrases contain only letters and spaces, and we altered
the phrases to use only lower case letters and American English spellings. The software used for our experiments (Figure 9) is designed to prompt the participant with the phrase
to be typed and record the response and timings for all of
the buttons pressed.
We found the mean entry rates for our ten participants forsession one were 8.2 wpm for multi–tap and 4.3 wpm for
chording. As sessions continued, the means improved andreached 19.8 wpm for multi–tap and 26.2 wpm for chordingby the end of the study (20 sessions, 400 minutes). Whileboth conditions showed improvement, the typing rates forthe chording condition rapidly surpassed those of multi–tap(Figure 2). After 20 session it is clear that the learning for
multi–tap has tapered off. As the regression curves indicate,
there is minimal improvement with each additional session.
Chording, however, is still showing strong signs of learning.
Learning rates of Chording and Multi-tap
30

25

20

WPM

(Figure 1). Note that the designation for left and right isfrom the user’s perspective while holding the keypad facing
away. As a result, there is a left–to–right mirror between
Table 1 and Figure 1.

15

10

5
Chording
Multi-tap
0
2

4

6

8

10
Session

12

14

16

18

20

Figure 2. Learning rates and exponential regression
curves for multi–tap and chording for 20 sessions [6].

Compared to other studies on mobile text entry using
similar keypads using a 3x4 grid of buttons, our results arevery encouraging (Table 2). Other studies have found thatmany text message users type near 8 wpm using multi–tap
and T9 [2, 8]. Some experts can reach higher rates, buteven new research text entry methods peak at 21 wpm [8].
In contrast after 400 minutes our participants reached an average typing rate of 26.2 wpm and were still improving.
In this paper, we present a follow–up study designed

Keyboard
Twiddler
desktop keypad
Nokia 3210 phone
desktop keypad
Nokia 3210 phone
Nokia 3210 phone
Nokia 3210 phone

Experience
400 min
550 minexpert
550 minnovicenoviceexpert

WPM
26.2
21.0
20.36
15.5
9.09
7.98
7.93

Average Chording Speed and Regression
55
50
45
40
35
WPM

MethodChording [5]LetterWise [8]T9 [2]
Multi–tap [8]T9 [2]
Multi–tap [2]Multi–tap [2]

30
25
20

Table 2. Comparison of mobile text entry rates using 3x4
keypads.

15
10
Chording speed
Original regression
Modified regression

5

to determine what chording rates our participants could
achieve and to confirm or refute the expert rates predictedby our regression curves. We also analyze the nature of how
the participants learned to type with chords. Finally, we examine the use of multi–character chords by our now expert
typists and the effects of limited visual feedback.

0
0

10

20

30

40

50

60

SessionFigure 3. Mean learning rates and regression curves
across participants.
Per user typing speed regressions
70

2 Learning to Chord

2.1 Towards Expertise
The first portion of our study is designed to confirm theregression curves from our previous work which indicate
the predicted learning rates for our participants. We collected data for approximately 20 additional sessions resulting in a total of 40 sessions or about 13 hours of practiceper participant. We ended this phase when our participants
were showing signs of expertise indicated by reduced rates
of learning. Figure 3 shows the average typing speed acrossparticipants. Also plotted is the original regression fromour first study and a modified regression based on the newdata from our five participants. The dip in the typing rate
at session 20 is the effect of the two week break between
our original study and this follow up. While there was adecrease, the participants rebounded by the next session.
Original regression : y = 4.8987x0.5781 , R2 = 0.9849
Modified regression : y = 5.3503x0.5280 , R2 = 0.9787

60

50

40
WPM

The study presented here continues with a very similar
procedure as in our previous work. For this study, five of
our original ten participants agreed to continue and we resumed testing after a two week intermission. The five otherparticipants from our original study declined to participate
due to the large additional time commitment required. Our
procedure was modified to focus our study on chording; wereplaced the multi–tap condition from our original experiment with a second chording phase. As a result, each time aparticipant came in we collected two 20 minute sessions ofchording data. For this experiment, we compensated eachparticipant at the rate of $0.33 × words per minute × accuracy.

30

20

Participant 1
Participant 2
Participant 3
Participant 4
Participant 5

10

0
0

10

20

30
Session

40

50

60

Figure 4. Per user typing rates and regressions

After 40 sessions the average typing rate for our participants increased to 37.3 wpm. This data shows that our original regression curve was slightly optimistic, predicting instead an average typing rate of 41.3 wpm. The difference
could be a result of the variance in individual typing rates.
Even though our regression fit to the mean typing rate of theparticipants is good, there are large differences in each individual’s typing rate. Figure 4 shows the typing speeds foreach of the participants by session. Also plotted are individual regression curves which have correlations of at least
0.96, indicating the data is well–fit.
Figure 5 shows the average error rate across participants
using Soukoreff’s and Mackenzie’s total error rate metric[13]. The metric accounts for both corrected and uncorrected errors made by the participants providing a single
total error rate. The final mean error is 6.2% and is slightly
above other typing studies with a similar experimental design [8]. As shown, participants rapidly reduce their error
rates as they initially learned to chord. As they learn to type
faster, their accuracy gradually decreases. We believe this isTotal Error Rate

Keypress interval times for participant 1

0.13

1000
In--air (2 button)
Press (2 button)
Hold (2 button)
Release (2 button)
In--air (1 button)
Hold (1 button)

0.12
0.11

800

0.1

Milliseconds

Error rate

0.09
0.08
0.07

600

400

0.06
0.05
200

0.04
0.03
0.02

0
0

5

10

15

20

25

30

35

40

45

Session

0

5

10

15

20

25

30

35

40

45

50

SessionFigure 5. Mean error rate across participants.

Figure 6. Keypress interval times for a single participant

an artifact of our experimental design as we did not directly
control for accuracy. Instead, each participant was compensated proportional to the product of his rate and accuracy.
As a result, the participants were rewarded if a small decrease in accuracy enabled a faster typing rate. A similareffect, where error rates gradually increase as participants
become experts, was shown by Matias et. al with the Half–QWERTY keyboard [10].

This introduces two additional intervals. The time between
the press of the first and second buttons of a chord is thepress interval while the time between releasing the first andsecond button of a chord is the release interval. Thus, thesequence of two button chord time intervals is in–air, press,
hold, and release, whereas single buttons only have in–air
and hold intervals.
Figure 6 shows per–session averages of these intervals
for a representative participant. This graph highlights whereusers spend their time in chording and suggests where theimprovements of learning have the most effect. These values were computed by taking the intervals for each chord
typed in sentences without any errors and then averaged forthe whole session on a per user basis. We did not includesentences with errors as we did not want to confound ourdata on intervals. Mistyping one chord can impact several
others, and it is not straightforward to incorporate the error
data with our individual time intervals. We intend to examine errors and their effects more thoroughly in future work.
All of the participants’ average in–air intervals for single and two button chords is shown in Figure 7. Thesetime intervals exhibit the largest effects of learning. For
novices, it is likely that this interval is dominated by the
cognitive effort associated with remembering how to typeeach character and how to move their fingers to the correct
position to type the letter. For experts, the delay becomesdominated by the time it takes to move the fingers from one
chord to another. Comparing the in–air interval for single
and two button chords reveals that, on a per user basis, thesingle button times are slightly faster and show better rates
of learning. However, the two button in–air interval tracks
the single button interval rather well. By the end of thestudy, the difference between the times on a per user basis
becomes much smaller. On average our participants take244ms to type a single button chord and 354ms for a two
button chord. The discrepancy is mostly due to a single participant (number two) who is lagging behind on learning the

2.2 Analysis of Learning Rates
In addition to confirming the learning rate for the Twiddler, our additional data allows us to examine how users
type on the Twiddler and to study the nature of the learning involved with chording. With a traditional keyboard,a character is generated by pressing and releasing a single
key. Chord typing, however, may involve pressing and releasing two or more buttons to generate a character. We
instrumented our experimental software to record the timeeach button is pressed and released for every chord. By examining the time intervals between each button press andrelease, we can gain insight into how novice users spend
most of their time while learning and what optimizations
we might make to aid expert users.
Typing a degenerate chord involving only a single button has one press and one release. This keypress has twointervals associated with it, in–air and hold. The first interval, in–air, is the time from when the last chord was completed (all of the buttons were released) to when the button
for the current chord is depressed; in other words, the time
when no keys are being held down. The other interval is
the hold time and represents the interval between the pressof the button and its release. We extended this notion ofintervals to two button chords as well. The interval during
which no buttons are pressed down is the in–air time, andthe time during which all of the buttons are depressed is thehold time. However, the buttons in the chord may not bepressed or released at the exactly the same moment in time.

In-air interval times for single-button chords

In-air interval times for multi-button chords

1000

1000
Participant 1
Participant 2
Participant 3
Participant 4
Participant 5

800

800

600

600

Milliseconds

Milliseconds

Participant 1
Participant 2
Participant 3
Participant 4
Participant 5

400

200

400

200

0

0
0

10

20

30

40

50

60

0

10

20

30

Session

40

50

60

SessionFigure 7. In–air interval times for single button chords (left) and two button chords (right).

two button chords. With additional practice his rates would
approach the others and the difference between the in-air
times for single and two button chords would decrease.

Press interval times for multi-button chords
1000
Participant 1
Participant 2
Participant 3
Participant 4
Participant 5

Participant 3 was significantly faster than the other participants and was typing at 67 wpm by the conclusion of
our experiments. To see if this might be attributable tohis simultaneous press strategy, we examined the data fromthe other five participants from our original study, who had
stopped after 20 sessions. Two of the subjects employed
the simultaneous press strategy, two of them the sequential strategy, and one started out sequential but appeared toswitch mostly over to the simultaneous strategy by the endof the twenty sessions. The participants who used the simultaneous press strategy were no faster than those who
used the sequential strategy. While simultaneous pressing
might not produce the fastest rates while learning, it shouldbe very beneficial for experts. At 60 words per minute, theaverage time to type one character is 200ms. Since the press

Milliseconds

800

Figure 8 presents the press interval, which is the time between the first and second buttons of a chord being pressed.
This interval is particularly interesting because it reveals
different typing strategies between users. A single participant (number 3) always pushes both of the buttons in achord at nearly the exact same time. The average delay
between the first and second button press is only 7.25ms
indicating that he always presses both buttons as one action. The other participants show a larger delay between
these button presses, indicating that they press the buttons
sequentially and likely learned how to press the chords in adifferent way than participant 3. The delay could be fromplanning and executing the two button presses in the chord
separately. The slower users may also initially wait for haptic feedback from pressing the first button. For these participants there is some learning associated with this interval;
however, it is not nearly as pronounced as the in–air timeinterval learning.

600

400

200

0
0

10

20

30
Session

40

50

60

Figure 8. Press interval times (two–button chords)

interval times varied up to 100ms by the end of this phaseand apply to more than 66% of the alphabet, pressing both
buttons of a chord at the same time should significantly increase the typing rate.
Our last two times are the hold interval and the releaseinterval (not shown). The average hold interval shows slight
improvement with practice, and in general single button
chords are held for slightly less time. At the end of thisphase the single button chords are held 98ms while two button chords are held 107ms. Perhaps participants spend theextra time to ensure that they avoid releasing the first finger
before the second one is depressed. Finally, while only one
participant pressed both keys of a chord simultaneously allof the participants rapidly learned to release both buttons ofa chord at approximately the same time. After about 10 sessions most of the users release both keys in less than 25ms.

3 Expert Usage
After about 40 sessions, enough data had been collectedthat we could be confident of our regressions’ predictions.

While performance was still improving, the rate of learning
had decreased enough that we considered our participants tobe expert users. At this point we continued our experimentwith two additional phases designed to investigate various
aspects of expert typing. We examine the possible benefitsof multi–character chords (MCCs) and the effects of typing
with reduced visual feedback (blind typing).

3.1 Multi–Character Chords
As mentioned previously, there are 255 possible chords
that can be typed on the Twiddler using the four fingers.
Of these, only a small subset are allocated to the alphabet
and punctuation needed to type English text. Some of theunused chords can be employed as multi–character chords(MCCs) which could generate any sequence of characters.
In the next phase of our experiment we wanted to determine if MCCs for short common words and suffixes would
improve our participants’ typing rates. Our hypothesis isthat MCCs would have a positive impact on typing rate because the number of button presses needed to type any given
MCC string, such as “the ”, would be reduced down to one
chord. Using a MCC would reduce the overall number of
keystrokes per character (KSPC) [7] as fewer keystrokes(button presses) would be needed to generate the same text.
We chose to investigate the benefits of MCCs by selecting 12 strings of at least three letters that are very prevalent in written English. The string were chosen using word
frequency data from the commonly used text corpus, theBritish National Corpus [3]. For this experiment we selected ‘for’, ‘and’, ‘the’, ‘ent’, ‘ing’, ‘tion’, ‘ter’, ‘was’,‘that’, ‘his’, ‘all’, and ‘you’ to be typed as MCCs. We assigned these strings to unused chords that did not involvethe index finger. As many of these strings are normally followed by a space character, this assignment enabled us to
add 12 extra MCCs that had a trailing space such as “the ”.
The buttons used for these chords are the same as the normal version, only the user also depresses the button used forspace (the right button operated by the index finger). Table
3 shows the keymap for the additional MCCs.
To introduce MCCs to our participants, we modified theexperimental software to highlight the next MCC that could
be typed. Our software has a diagram of the Twiddler keypad that acts as a guide to help the users learn the basicalphabet keymap. We modified the diagram so that the keys
needed for the MCC are also highlighted (Figure 9). We instructed our participants to use the MCCs and to encourage
their use, we modified the error calculation so that typing
the MCC string letter–by–letter counted against the participant’s accuracy.
The effect of MCCs on our participants’ typing rates
is mixed. Initially, our participants slowed down as they
started to learn MCCs. For the first session, the averagetyping speed dropped to 83.5% of what it had been. How-

Buttons
0LL0
0MM0
0RR0
00LL
00MM
00RR
0LLL
0MMM
0RRR
0L0L
0M0M
0R0R

String
‘for’
‘and’
‘the’
‘ent’
‘ing’
‘tion’‘ter’‘was’‘that’
‘his’
‘all’
‘you’

Buttons
RLL0
RMM0
RRR0
R0LL
R0MM
R0RR
RLLL
RMMM
RRRR
RL0L
RM0M
RR0R

String‘for ’‘and ’‘the ’‘ent ’‘ing ’‘tion ’
‘ter ’‘was ’‘that ’
‘his ’‘all ’‘you ’

Table 3. Keymap for new multi-character chords (MCCs)
with and without trailing space.

Figure 9. Our experimental software showing the use ofMCCs; “ing ” is the MCC to be typed (‘R0MM’) and ishighlighted in blue.

ever on the fifth session, the average speed was 97.1% of the
pre–MCC speed, and by the tenth session it was 104.5% andcontinued to improve. Even though the rate increased beyond the typing speed just before the introduction of MCCs,
the participants were still slowly learning. If we had not introduced MCCs and just had our participants continue topractice, we would have expected the rate to increase to approximately 112% based upon our regressions. As a result
we cannot attribute the overall increase in typing rate solely
to the effects of MCCs.
To better understand the effects of MCCs we compared
the amount of time participants needed to type a new multi–
character cord compared to the time to type the chords
for the alphabet which they already knew. In general, ourmulti–character chords might be slower because they involve more buttons (up to four) while the chords for the alphabet require at most two buttons. At the end of the MCC
phase of our experiment, our participants were taking an average of 596ms to type each MCC, while two button alphabet chords took only 354ms and single took 244ms. We an-ticipate that once our participants mastered typing the newmulti–character chords, the time per chord would drop andincrease the overall typing rate. Looking at the time to generate the same string with and without the use of MCCs isalso interesting. As mentioned, typing a MCC takes an average 596ms; while in contrast, typing out the same strings
letter–by–letter takes 1018ms. As a result, there is a net
savings in time using MCCs because typing the sequencewith regular chords takes longer than typing the one multicharacter chord.
An analysis of our phrase set revealed that 17.5% of
the characters in our phrase set can be typed with MCCs.
Weighted by the frequency of MCCs in our phrase set, thiswould correspond to about an 8% increase in average overall typing speed. This effect would likely be more pronounced using a phrase set more representative of English
on a word frequency basis instead of letter frequency [9] andas our participants master the new multi–character chords.

3.2 Blind Typing
In a mobile environment, a user’s visual attention may
be diverted away from her display while entering text. Silfverberg examined the effect of visual and tactile feedback
when using a mobile phone keypad [12]. He found that
limited visual feedback combined with low tactile feedback
hinders a user’s average error rate; on the other hand, goodtactile feedback results in much a smaller decrease in accuracy.
Inspired by these results and our own anecdotal experience of typing with limited visual feedback, we designed
the last phase of our chording experiment to evaluate blind
typing on the Twiddler. We have a 3 x 5 design with 3 conditions (normal feedback, dots feedback, and blind) over 5
sessions of typing. Each condition lasts 15 minutes. Our
normal feedback condition displays the text typed under
the phrase presented to the participant as shown in Figure 9, but without MCC highlighting. As the Twiddler isheld with the keypad facing away from the user, this condition corresponds most closely to Silfverberg’s indirect visual feedback condition. For our dots condition, we display
periods for each character typed instead of the transcribed
text. Thus, participants see their position in the suppliedphrase, but not specifically what they type. This condition
is designed to simulate monitoring text typed without being
able to actually read the letters such as seeing the text on aheads–up display using only peripheral vision. Finally, theblind condition does not show any on–screen indication ofwhat is typed and mimics Silfverberg’s no visual feedback
condition. For both the dots and blind conditions, participants are shown their transcribed text and error statistics
when they press enter at the end of the phrase. We predictedthat like Silfverberg, reducing the visual feedback would
limit our participants’ typing rate and accuracy.

Participant
NormalDots
Blind

Typing Rates (wpm)
12
3
4
51.8 37.6 64.2 36.2
51.7 37.5 67.2 36.0
53.7 37.5 67.7 36.6

5
41.8
43.1
41.7

Participant
NormalDots
Blind

Percent Errors
1
2
3
5.61 5.62 7.01
4.82 5.02 5.75
5.03 4.63 5.90

5
6.64
5.83
5.44

4
9.83
9.26
8.89

Table 4. Per participant typing and error rates for thethree conditions. Bold indicates a statistically significant
difference at the 0.05 level between that condition and thenormal condition for that user.

Surprisingly, changing the visual feedback did not hinder
the participants in their typing as expected. In some cases
typing rates and error improved with the reduced feedback.
Table 4 shows the change in speed and the error rate for theblind typing conditions. Values where a two–tailed t–test
showed a statistically significant difference at the 0.05 levelfrom the normal condition are marked with bold. Whenever
there is a statistically significant difference between normal
typing and one of the reduced feedback conditions, the reduced feedback condition shows an improved typing rate ora reduced error rate. More work will be needed to explore
which factors affected this result.

3.3 Expert Typing Rates
By the end of all of our experiments, our participants
completed an average of 75 sessions which corresponds toapproximately 25 total hours of practice. Figure 10 shows
the typing rates for our participants across all of our experimental conditions described above. The final averagetyping rate reached 47 wpm and unexpectedly our fastest
participant achieved a rate of 67.1 wpm which is fast as thethird author, an expert of ten years.

4 Future WorkIn the future, we would like to create a model of Twiddler chording which accounts for finger motion and effects
between chords. Our analysis of learning rates from Section 2.2 is a first step. Ideally, this model would enable us
to evaluate different keymaps and optimize them for various tasks such as maximizing expert performance or easing learning. We also want to continue our study of multi–
character chords to determine their effect on overall learning and typing speed and further examine the types of errors
made while chording. Finally, we are interested in increasing the appeal of the Twiddler for novice users. We aredeveloping a tutor to instruct people how to chord and eval-BusinessWeek, June 2004.

70[2] C. L. James and K. M. Reischel. Text input for mobile
devices: comparing model prediction to actual performance. In Proceedings CHI, pages 365–371. ACM
Press, 2001.

60

50

WPM

40

[3] G. Leech, P. Rayson, and A. Wilson. Word Frequencies in Written and Spoken English: based on the
British National Corpus. Longman, 2001.

30

20[4] M. Lindstom. Message madness our big chance. SMH
http://www.smh.com.au, February 2002.

P1 Chording
P2 Chording
P3 Chording
P4 Chording
P5 Chording

10

0
0

10

20

30

40

50

60

70

80

SessionFigure 10. Data across all phases of experiment for all 5
participants.

uating the best mechanisms to teach chording. While theTwiddler offers very fast expert typing rates, we believe atutor may improve the initial typing rate so that it is as fastas other mobile typing methods such as multi–tap. We arealso exploring incorporating Twiddler style chording into a
mobile phone that would offer better mass appeal.

5 Conclusion
We have analyzed various aspects of expert chording
on the Twiddler keyboard including text entry speed, theeffects of visual feedback, and the use of multi–character
chords. We found that our participants reached an averagetyping rate of 47 wpm while our fastest participant reached
67 wpm. Our data on multi–character chords indicates thatthey could provide even higher typing rates. We examined
how our participants learned to chord, showing most of the
speed increase associated with learning occurs during thein–air time interval. We also found a difference in strategy
of how our participants press the buttons of a chord. The
blind typing data shows that the Twiddler can be used effectively with limited visual feedback which is important ina mobile environment. Given the expert users’ high text entry speeds and ability to touch type, chording seems to be a
viable mechanism for text entry on future mobile devices.

6 Acknowledgements
This work is funded in part by NSF Career Grant#0093291 and the NIDRR Wireless RERC. We would liketo thank S. MacKenzie and W. Soukoreff for offering theirphrase set for use, and S. Zhai and M. Silfverberg for theircomments.

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