one-dimensional handwriting: inputting letters and words...

One-Dimensional Handwriting: Inputting Letters and Words on Smart Glasses

Chun Yu Ke Sun Mingyuan Zhong Xincheng Li Peijun Zhao Yuanchun Shi Key Laboratory of Pervasive Computing, Ministry of Education

Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

{chunyu, shiyc}@tsinghua.edu.cn, {k-sun14, zhongmy14, lixc12, zhaoym13}@mails.tsinghua.edu.cn

ABSTRACT We present 1D Handwriting, a unistroke gesture technique enabling text entry on a one-dimensional interface. The challenge is to map two-dimensional handwriting to a reduced one-dimensional space, while achieving a balance between memorability and performance efficiency. After an iterative design, we finally derive a set of ambiguous two-length unistroke gestures, each mapping to 1-4 letters. To input words, we design a Bayesian algorithm that takes into account the probability of gestures and the language model. To input letters, we design a pause gesture allowing users to switch into letter selection mode seamlessly. Users studies show that 1D Handwriting significantly outperforms a selection-based technique (a variation of 1Line Keyboard) for both letter input (4.67 WPM vs. 4.20 WPM) and word input (9.72 WPM vs. 8.10 WPM). With extensive training, text entry rate can reach 19.6 WPM. Users’ subjective feedback indicates 1D Handwriting is easy to learn and efficient to use. Moreover, it has several potential applications for other one-dimensional constrained interfaces.

Author Keywords Text entry; one-dimensional input; unistroke gestures.

ACM Classification Keywords H5.2. Information interfaces and presentation: User inter-faces — Input devices and strategies.

INTRODUCTION Nowadays, many new smart personal devices are emerging. Such devices typically have a constrained input interface due to the limited size of the form factor. Consequently, text entry is difficult on these devices, which prohibits their broader use. In this paper, we focus on one-dimensional text entry for devices that have only one-dimensional input

signals, or the input capacity on one dimension is much greater than that on the other. Examples include the touchable spectacle frame of a smart glass (e.g. Google Glass), the edge of a side screen of a smart phone (e.g. GALAXY Note Edge), and a smart wristband.

Researchers have proposed various techniques to enable text entry on constrained interfaces (e.g. a keypad, a joystick, a tracking ball, and air interaction etc.). These techniques include coding letters [16], unistroke gestures [2], ambiguous keyboards [18], and gesture keyboards [12, 31]. However, most of these techniques are for two-dimensional constrained interfaces or interfaces with a limited number of buttons or gestures.

In this paper, we present 1D Handwriting, which enables users to perform unistroke gestures on a one-dimensional interface to input text. We research, implement and evaluate 1D Handwriting with Google Glass, a representative one-dimensional input device. The biggest challenge encountered is that many letters look similar, if not identical, after projected into a reduced one-dimensional space. Therefore, our goal is to derive a set of one-dimensional gestures that strikes a balance between memorability, input accuracy and input speed.

After a careful and iterative design, our result is a set of ambiguous unistroke handwriting gestures based on sub-stroke of two-level lengths. To complete the input, we design a one-dimensional gesture recognition algorithm that classifies input strokes in a probabilistic way. For letter input, we design a smooth transition gesture allowing the user to first gesture a stroke (representing a group of letters) and then select the target letter (within the group) without lifting his/her finger. For word input, we use a Bayesian approach that takes both the probability of gesture and the language model into account to interpret users’ input.

To evaluate the performance of 1D Handwriting, we conduct three lab experiments. Results show that 1D Handwriting provides immediate usability: With limited training, users can achieve 4.67 WPM for letter level input (error rate = 0.78%) and 9.72 WPM for word level input (error rate = 0.25%). These speeds are comparable to those proposed for two-dimensional constrained interfaces, and significantly outperforms the one-dimensional selection-based technique (the Baseline in our experiments) by 11.4%

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. Copyrights forcomponents of this work owned by others than ACM must be honored.Abstracting with credit is permitted. To copy otherwise, or republish, topost on servers or to redistribute to lists, requires prior specific permissionand/or a fee. Request permissions from [email protected]. CHI'16, May 07-12, 2016, San Jose, CA, USA © 2016 ACM. ISBN 978-1-4503-3362-7/16/05…$15.00 DOI: http://dx.doi.org/10.1145/2858036.2858542

How Fast Can You Type on Your Phone? #chi4good, CHI 2016, San Jose, CA, USA

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eight keys, with each key overloaded with multiple letters. The Minuum keyboard [21], based on 1Line Keyboard, enables text entry on Google Glass. However, there is only a demo video describing it; no implementation details nor performance data are available. Circle keyboard [20] organizes letters into a circle in alphabetical order. Users rotate a Wiimote to select target letters. The text entry rate was 10.2 WPM. Walmsley et al. [22] applied the source-channel approach to Circle keyboard to allow fast and imprecise input. Experiment results showed expert users could reach 21WPM after 6.7 hours practicing.

In sum, an input interface can be constraint in terms of the number of input signals (keys and usable gestures) or the number of dimensions of the input space. In this work, we target the constraint interface where touch can be performed only in one-dimensional space. Besides, we focus on handwriting approaches rather than selection-based ones for text entry.

STUDY 1: DESIGN OF 1D HANDWRITING GESTURES The goal was to gain an understanding about user acceptance of 1D Handwriting gestures as well as the input capacity of Google Glass, the device we used to experiment our idea in this research. To achieve this, we first derived a set of 1D Handwriting stroke gestures, and then asked individual participant to assess their intuitiveness by performing them on Google Glass. Based on the subjective and objective results, we agreed on the design guidelines for the final handwriting gestures, and revised our design.

Phase 1 —Initial design of handwriting gestures In our first trial, we adopted a user-participatory approach [25]. We recruited eight participants and asked them to design a set of one-dimensional gestures for Google Glass. The process lasted for one hour for each participant to design gestures for all twenty-six letters of the alphabet. In the end, four participants proposed a code-based design, while the other four proposed a handwriting design. Unfortunately, none of the obtained gesture sets was satisfactory, because they were not memorable. This result revealed the difficulty of designing appropriate 1D Handwriting stroke gestures, especially within a very limited time. However, according to the post-experiment interviews, participants (including those who proposed the coding-based design) consistently agreed that the handwriting design was much more recognizable and memorable than the code-based design.

We then decided to design a set of handwriting stroke gestures by ourselves. The guideline was to design a 1D Handwriting stroke gesture for each letter to best mimic their two-dimensional counterparts. Figure 4 summarizes the handwriting gestures. For some letters, the design was easy and intuitive. For example, the handwriting stroke gesture of “w” should be a four-stroke gesture “back—forward—back—forward”. However, for others it was not an easy task since several letters were similar, if not identical, when projected into a reduced one-dimensional

space. In such cases, we designed strokes with more details (e.g. adding a short stroke at the end to distinguish “q” from “y”), or use a dot to distinguish them, such as “o” and “v”, “b” and “k”, “i” and “s”, and “j” and “l”. The design of handwriting gestures employed strokes of three levels of lengths (i.e. short, medium and long) and a dot.

Figure 4. Handwriting gestures used in Study 1. Dots (“·”) were included in this design. However, these were

consequently dropped in favor of faster input speed. Arrows represent the directions for each sub-stroke in a gesture. They

are drawn continuously in a gesture.

Phase 2 — User assessment and data collection

Apparatus We used Google Glass for this experiment. As shown in Figure 5, there is a touchpad mounted on the right spectacle frame. The touchpad (7.0 centimeters long and 0.8 centimeters wide) supports up to three touch points with an input resolution of 1366×160. Although Google Glass is not a strict 1D input device, the vertical space is very limited for input. In addition, there is a virtual screen (resolution: 640×360) and a built-in speaker on the device. The Google Glass system was XE 22 running on Android 4.4.4. We developed a software that recorded all touch events, including time and coordinate information.

Figure 5. The touchpad configuration of Google Glass

Design and procedure Twelve participants (six males and six females; Age: 18-25) were recruited from Tsinghua University campus. None had previously used a Google Glass. The experiment took about one hour to complete. At first, each participant was asked to memorize the handwriting gesture set (Figure 4). Then, they were instructed to perform these gestures (five times per letter) on Google Glass at their comfortable speed. For reference purposes, a sheet containing every letter-gesture pair was placed beside participants in case they forgot a gesture during the experiment. At the end of the


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gure 9. A variatied key (“edc”) ithe interface de

se both 1Lineguous” for ation to quantifed a dictionar

ource code ofates the resultoard exhibited ularies containments, we unary.

igure 10. Accur

STUDY 2: INPer to evaluate tng letters, we ne Keyboard.

atus and partin participants 18-30) were

us. None of the.

Figure 11. The

ion of 1Line Keis highlighted. Fesign is similar

Keyboard aninputting wofy their abilityry (with frequf the Androidts. Both 1D high Top-1 a

ning as many aused the top

racy for vocabul

PUT LETTERSthe performanccompared its

icipants (eight males recruited fro

em had previou

experiment sett

eyboard. The cuFor consistencyto 1D Handwri

nd 1D Handwrords, we pery to specify tarency data) derd Keyboard. Handwriting accuracy (ove

as 178,000 wor10,000 word

laries of varyin

S ce of 1D Handperformance a

and eight femom Tsinghua usly used a Go

ting of User Stu

urrently y purposes, iting.

riting were rformed a rget words. rived from Figure 10 and 1Line r 95% for rds). In the ds as the

ng sizes

dwriting for against that

males; aged University

oogle Glass

udy 2


76

Fsearrsth

DWo(fMsthinbrigt

BptfGBwDpoetm

TthcpppwlKGmst

RTapwbop

Figure 11 illusetting. The syenter on a laptaway from therested their elrequired inputssynchronized ahe laptop PC th

Design and PrWe used a witonly one indep(1D Handwritinfor each parMacKenzie ansame phrases fhe order of phrn one session.

block containirequired to breagnored for pechnique was c

Before the expparticipant. Weechniques wa

familiarize theGlass, which Before each tewarm-up sessiDuring the exphrases as fast of the experievaluation forechniques. Th

minutes.

The warm-up shat of 1Line

containing eveparticipants. Npractice the performing eacwere asked to etters appeared

Keyboard, partGlass until thmovement coselecting/deletiechniques laste

Results The main depeand uncorrecteprovide more dwe further divbetween the coof the next performing the

ustrates the eystem displayetop PC. Partici

laptop PC. Thlbow onto thes. The softwareall touch eventhrough Wi-Fi.

rocedure thin-subject dependent factor ng and 1Line Krticipant was d Soukoreff's for the two terases randomiz Sessions cons

ing two phrasak after finishinpractice. The counterbalance

periment, we e did not informs ours. Moreomselves with took approxi

echnique was ion with the xperiment, sub

and accurate aiment, particirm and provhe total experim

session of 1D Keyboard. F

ry letter-gestuext, we asked strokes on

ch gesture fivcomplete a shd twice in a raticipants practhey became ontrol and ng a letter). ed for about th

endent measureed errors), inpdetailed inform

vided its input ompletion of th

stroke), gestue handwriting

experiment ened phrases foripants sat apprhey wore a Ge desk while e on Google Gts during the e

esign for this ewas the Tech

Keyboard). Thrandomly g

phrase set [14chniques per pzed. Each technsisted of six bses for input.ng a block. Thpresentation

ed.

introduced itsm participants wover, we allowthe user intermately five tpresented, patested text en

bjects were reas possible. Fiipants filled

vided feedbackment lasted fo

Handwriting For 1D Handwure pair was fi

participants toGoogle Glasse times. Fina

hort practice taandom order. Aiced input phrfamiliarized wother input

Warm-up sehirty minutes.

es were input eput speed and mation about 1

time into elahe previous strouring time (stroke gesture

nvironment anr participants roximately 0.5oogle Glass anperforming th

Glass logged anexperiment wi

experiment. Thhnique employhe tested phrasgenerated fro4]. We used thparticipant, winique was testlocks, with ea Subjects we

he first block worder of ea

s goals to eacwhich of the twwed subjects rface of Googto ten minute

articipants had ntry algorithmequired to inpinally, at the en

a NASA-TLk on the twr roughly nine

was longer thwriting, a sheirst presented o memorize ans by correctally, participanask where all 2As for the 1Linrases on Googwith the tou

gestures (e.essions of bo

errors (correctinput time. T

1D Handwritinapsed time (timoke and the sta(time spent oe) and selectio

nd to

5m nd he nd ith

he ed

ses om he ith ed ch

ere was

ch

ch wo to

gle es.

a ms. put nd

LX wo ety

an eet to nd tly nts 26 ne

gle ch .g.

oth

ed To ng, me art on on

time (importnecessagesturelocatiohandw

CorrecFigure error ra

There werror rlow unHandwThe reif they

ANOV(F1,15=6correctTechniParticipthan w(SD=3performp<.005effect p=.29)

Figur

Input SFigure over thincludeaverag1D HaKeyboinput a4.20 WHandwaveragblock significtechniqKeyboeffect.(F4,60=

(time spent otant to note ary pause bees (to completon) and the t

writing stroke ge

cted and uncorr12 illustrates

ates migrated w

was no significratio (F1,15=1.7ncorrected erro

writing and 0.4eason was that

noticed it on t

VA showed 68.92, p<.000ted error rique Block wapants had more

with 1Line Key.69%). Howevm better (i.e. h5) with 1D Hafor 1Line Key.

re 12. The error

Speed 13 shows the

he five blocked the time u

ge, participantsandwriting an

oard. If we conat 4.67 WPM

WPM (SD=0.6writing was 8ge (F1,15=5.31,

(F1,15=8.70, pcant main effeques (1D Han

oard: F4,60=15.3There was no 1.37, p=.26) on

on performingthat the elap

etween succeste a stroke gestime participaestures.

rected errors s how the cowith blocks for

cant effect of T76, p=.20). Boor ratios: 0.78

48% (SD=0.65participants te

the virtual scre

significant 01) and Blockratio. The as also significe corrected err

yboard, 17.67%ver, through prhaving less corrandwriting. In yboard was no

rs for both techntesting result

e text entry raks. The calculusers spent ons input at 4.04

nd 3.71 WPMsidered only th(SD=0.61) w

61) with 1Lin8.9% faster th

p<.05), and p<.01). Mean

ect of block onndwriting: F4,60

39, p<.0001), interaction eff

n text entry rat

g the selectipsed time incsive handwritsture and resetants spent to

orrected and ur letter-level in

Technique on uoth techniques8% (SD=1.12%5%) for 1Line ended to correcen.

effects of k (F4,60=3.80,

interaction cant (F4,60=4.22rors with 1D H% (SD=6.10%)ractice, participrected errors) (contrast, such

ot significant (

niques with thets.

ates for both lation of text n correcting e4 WPM (SD=

M (SD=0.46) whe last block, p

with 1D Handwe Keyboard. H

han 1Line Ke11.2% faster

nwhile, we on text entry rat0=14.50, p<.00suggesting th

ffect of Techniqtes.

on). It is cludes the ting stroke t the finger

recall the

uncorrected nput.

uncorrected s exhibited %) for 1D Keyboard.

ct mistakes

Technique p<.01) on effect of 2, p<.005).

Handwriting vs. 5.51%

pants could (F4,60=4.68,

a learning (F4,60=1.39,

e statistical

techniques entry rate

errors. On =0.58) with with 1Line participants writing and Hence, 1D eyboard in in the last

observed a es for both 001; 1Line he learning que Block


77

i

InIti(Efinscsptile

Fslinthmdgwd“pgtir

Figure 13. Textand 1Line Keyin the 4th block

resulted fro

nput times of 1In average, elaime was 504m

(SD=151) inclElapsed time, gfor 45%, 16% as, the majority

next letter, recselecting lettercould be impsignificant effep<.0001), gestuime (F4,60= 11.earning curve

elapsed time an

Figure 14. Elaacross five bloc

Furthermore, aselection time fetter. From Fncreased as thehe structure o

medium lengthdependent on gestures as welwas no obvioudetected that fo“z”) elapsed participants spegesture. Interesime was short,

rather large.

t entry rates acryboard. There w

for 1D Handwrom uncontrolled

1D Handwritingapsed time wasms (SD=96), anluding 300ms gesturing timeand 39% of th

y of input timecalling the hans. Fortunately,

proved (Figureects of Block uring time (F4,6

04, p<.0001). Aof gesturing ti

nd selection tim

apsed time, gestcks for letter-lev

analysis of elapfor individual igure 15, we e number of suf sub-strokes i

h sub-strokes). the number oll as the order ous pattern foror some letterstime was relent more time rstingly, for the , both gesturing

ross blocks for was a sudden periting. We interd factors in the

g s 1397ms (SD=nd selection tim

spent on swe and selectionhe input time ree was spent onndwriting stro with practicee 14): RM-A

on elapsed t60= 5.41, p<.00A noteworthy ime was not asme.

turing time andvel 1D Handwr

psed time, gesletters was concan see that,

ub-strokes or thincreased (withSelection tim

f letters repreof letters insidr elapsed times that were selatively high, recalling its haletter “j”, althog time and sele

1D Handwritinerformance droprpret it as a nois

experiment.

=354), gesturinme was 1206m

witching moden time accountespectively. Thn considering thoke gesture, an, all input tim

ANOVA showtime (F4,60=8.701) and selectioaspect is that ths steep as that

d selection time riting in Study 2

sturing time annducted for ea, gesturing timhe complexity h both long an

me was primariesented by inpe a group. Thee. However, wldom input (e.indicating th

andwriting strokough the elapsection time we

ng p se

ng ms es. ed

hat he nd

mes ed

73, on he of

2.

nd ch

me of nd ily put ere we .g. hat ke ed

ere

Figurediffere

SubjecNonpacompaHandwp<.000much other m

Althouto permanagpracticintervigestureIn addifatigueon the 1D Hascreen participThese1D Han

USER The goHandwsixteenrecruiteseven appara

The dethat paletter-lrequire

Prior tohandwand to that pa

e 15. Elapsed timent letters. The c

same handwr

ctive feedbacksarametric Wilcared to 1Linwriting to be 01). On the othbetter evaluat

measures were

ugh at first it srform the hanged to quickly ce rounds for ew, every parties to be intuiti

dition, most pare with 1Line Ke keyboard to andwriting req

while performpants were mfindings sugge

andwriting.

STUDY 3: INPoal of this stu

writing and 1Lin participants ed again for tto fourteen da

atus as in Study

esign and procarticipants perflevel input. Ined to enter 18 p

o the experimewriting stroke ge

write them dowarticipants cou

me, selection timcolors separate

riting stroke for

s coxon signed-rne Keyboard,

more mentalher hand, theytion (z=-42.0, not statisticall

seemed difficulndwriting strok

master the ter each letter. icipant reporteve to recognizrticipants repo

Keyboard since control cursor

quired a lesserming stroke g

more familiar wested eyes-free

PUT WORDS udy was to evine Keyboard

who participthis study. Thays after Study 2.

cedure were siformed word-ln this experimphrases (divide

ent, participantestures for all 2wn on a piece

uld correctly r

me and gesturine letters with resr easy comparis

rank tests sugg, participantsly demanding

y gave 1D Hanp<.01). Diffe

y significant.

lt to rememberke gestures, pechnique in les

In the post-eed 1D Handwrize and easy to orted higher lev

more focus war movement. Ir degree of focestures, espec

with the strokee input was po

valuate and cofor inputting w

pated in Stude experiment

dy 2. We used

imilar to Studylevel input as oment, participed into nine blo

ts were asked to26 letters of thof paper. Resu

recall 89% of

ng time for spect to the on.

gested that felt 1D

g (z=-52.5, ndwriting a erences on

r and learn participants ss than ten experiment iting stroke remember. vels of eye as required In contrast, cus on the ially when e gestures. ssible with

ompare 1D words. The dy 2 were took place

d the same

y 2, except opposed to pants were ocks).

o recall the he alphabet, ults showed

the stroke


78

gwtredo

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RTas

CFe

TeloH

Wceas(s(

F

InFo

I1K1la9

gestures. Four well were “x”raining each p

each letter in adays, this resulof 1D Handwri

Participants fama warm-up sesso fifteen minu

required to inpThe total exper

Results The main depeand uncorrectedstandard deviat

Corrected and uFigure 16 illuerror rates migr

There was no serror ratio (F1,

ow uncorrecteHandwriting an

We observed acorrected errorerror ratio wasand 11.87% (SDsignificant eff(F7,105=1.15, p=significant int(F7,105=0.46, p=

Figure 16. The

nput Speed Figure 17 showover the eight b

In average, par1D HandwritinKeyboard. Tha1Line Keyboarast two blocks

9.72 WPM (SD

letters that pa, “d”, “j” andparticipant hadverage) and thlt positively inditing stroke ges

miliarized themsion before theutes for each

put phrases as riment lasted fo

endent measured errors) and intions. Therefor

uncorrected erstrates how thrated with bloc

ignificant effec,15=1.21, p=.29ed error ratiosnd 0.07% (SD=

a marginal signr ratio (F1,15=s 17.38% (SDD=5.81%) for fect of Block=.034), suggeteraction effe

=.86) was obser

corrected and utechniques a

ws the text enblocks.

rticipants inputng and 7.14 Wat is, 1D Handwrd in average (Fs were consideD=2.67) for 1D

rticipants could “t”. Consided (no more thhe interval of sdicated the goostures.

mselves with the task, which l

technique. Pfast and accur

or about eighty

es were input enput speed. Ale, no outliers w

rrors he corrected acks for word-le

ct of Techniqu9). Both techns: 0.25% (SD==1.42%) for 1-L

nificant effect o4.51, p=.051)

D=8.67%) for 1Line Keyboa

k on correctsting the learect of Techrved.

uncorrected erracross blocks

ntry rates for

t at 8.84 WPMWPM (SD=1.writing was 23F1,15= 6.92, p<ered, text entryD Handwriting

ld not remembering the limitehan 20 times f

even to fourteod memorabili

he techniques asted around tarticipants werate as possibl

y minutes.

errors (correctll data are withwere removed.

and uncorrectevel input.

e on uncorrectniques exhibit=0.07%) for 1Line Keyboard

of Technique o. The correct1D Handwritinard. There wasted error rat

rning effect. Nhnique Blo

ror rates for bot

both techniqu

M (SD=2.79) wi.21) with 1Lin3.8% faster tha

<.05). If only thy rates raised

g and 8.10 WP

ber ed for en ity

in en

ere le.

ed h 2

ed

ed ed

1D d.

on ed ng s a tio No ck

th

ues

ith ne an he to M

(SD=1was 20blocks

ANOVtext enlearninof Tech

F

Input tiOn ave(SD=6be intewhen c1Line 1D HaStudy techniq

Figua

Auto-coAuto-c1D Hadifferenp<.000input vphrase5.17 WThe recompleHandwstroke the lap

.34) for 1Line0.0% faster th (F1,15=5.86, p<

VA also showedntry rates (F7

ng effect. Therhnique Block

Figure 17. The

imes erage, the elaps

66) longer thanerpreted as thecompared to tKeyboard, the

andwriting was2, we observe

ques (p<.05), a

ure 18. Slide timacross eight blo

ompletion completion accandwriting an

ence was st01). In other wvia auto-comps at 8.30 WPM

WPM (SD= 0.8eason why partetion for 1Lin

writing, as pargestures, they

ptop PC screen

e Keyboard. Than 1Line Ke<.05).

d a significant7,105=11.43, p<re was no sign

k on text entry r

learning curve

se time of 1D n that of 1Linee recall time fthe slide time e gesturing tims much smallered significant as shown in Fig

me, gesturing timocks for both tec

counted for 6.nd 38.10% fortatistically sigwords, if we

pletion, particiM (SD=2.70) f86) for 1-Line kticipants took

ne Keyboard warticipants perfy tended to loon instead of th

That is, 1D Heyboard in th

t main effect o<.0001), suggnificant interacrates (F7,105=1.2

of both techniq

Handwriting we Keyboard, wfor gestures. In

(1,593 ms, Sme (427 ms, Sr. Similar to thelearning effectgure 18.

me and elapsed chniques in Stu

51% of inputr 1Line Keybgnificant (F1

did not consiipants would hfor 1D Handwkeyboard over more advantag

was as followsformed the hok at the test phe Google Gla

Handwriting e last two

of Block on gesting the ction effect 25, p=.28).

ques.

was 289 ms which could

n addition, D=132) of

SD=38) for e results of ts for both

time and udy 3.

letters for board. The ,15=191.49, ider letters have input

writing, and all blocks.

ge of auto-s: With 1D handwriting phrases on

ass display.


79

Tnpcpthwpath

UTbfinatwn[wsao

RWa1lrla

AorposMthsru

LTp

They only turneeded to selecparticipants cocontrol cursor mparticipants to he post-experim

with participanparticipants maauto-completioheir eyes reste

USER STUDY The goal of thibe following exfive participantnput a single

and Soukoreff’wo trials were

number of word6]. However, i

was based on aserved as a rouauto-completioof a word with

Figure 19. The

Results showedWPM (SD=3.5achieved 24 W16 WPM. As earning effect

rate. In averagast block than

According to pobservations, arates was the liperforming fasovershot the toslow down finMoreover, we he design of

speed and expreasonably reflused 1D Handw

LIMITATIONS AThis research point to the dire

rned to the vct words. In constantly lookmovement. Thnotice the au

ment interviewnts. We think ay be more liken function witd on the virtua

4: EXTENSIVEs study was to xtensive trainits who participphrase (random’s phrase set [1e ignored as prds, the trainingit is important

a single phrase,ugh estimation.n so that partichandwriting st

learning curve

d the mean tex50) for the last

WPM, while theFigure 19 shof Block (F9,6

ge, participantsin the first one

participants’ suba major factor imited input cast gestures, thouchpad. In sunger movemenconfirmed wiexperiment: P

perience obtainlect their profiwriting for a lon

AND FUTUREhas the follo

ection of future

virtual screen ontrast, with 1

ked at the viherefore, it wasuto-completion w, we confirme

in actual reaely to take full th 1D Handwr

al screen.

E TRAINING estimate how

ing. To achievpated the Studymly selected f14]) for twelveactice. By focu

g time could beto note that gi

, the obtained s In this study, cipants had to troke gestures.

e of 1D Handwr

xt entry rate cophrase. The fae slowest part

hows, there w3=6.16, p<.000

s entered 33.95e.

bjective feedbafor prohibiting

apacity of Goohe finger ofteuch cases, parnts to produceth participants

Participants thned in the ex

ficient input spnger period.

E WORK owing limitatioe works.

whenever th1Line Keyboarirtual screens more likely f

suggestions. ed this differenal-life scenarioadvantage of thriting by havin

fast a user coue this, we asky 3 to repeatedfrom MacKenze times. The firusing on a smae greatly reduciven the practispeed could onwe switched oinput each lett

riting in Study 4

ould reach 19.6astest participaicipant achiev

was a significa01) on text ent5% faster in th

ack and our owg high text entgle Glass. When went off rticipants had e correct inpus with regardhought the inpxperiment coupeed if they h

ons, which al

ey rd, to

for In

nce os, he ng

uld ed

dly zie rst all ed ice nly off ter

4.

61 ant ed

ant try he

wn try en or to ts. to

put uld ad

so

First, ofor lowmore cnon-Labetter band 1Dto remeoverloaaddrescharactthe “fllower c

Secondparticipand rethere iemploy[1]) to to testtraining1D Hlearnin

Third,handwbe notadvantas the sensorshandwsmartpedge),

Finallyinputtin1D hakeyboa

CONCWe exform interfachandwanalyzeHandwimplemGlass. suggesrememfeasibi

ACKNO

This wof ChiTsinghand No

our current 1Dwer-case Englcharacter setsatin alphabets.be an easy-to-r

D strokes; otheember. In addiading a single s this with ter sets. For elip up” gesturcase, or even b

d, regarding lpants could lea

ecall 89% of tis still space foy a well-desigfacilitate gestu

t the performg. Now, in allandwriting di

ng time.

in this researcwriting. Howev

t optimal for tage of the vert

three-point tos) for input.

writing techniqphones with a

smart wristban

y, in current resng letters. It isandwriting forard. This might

LUSION xplored the po

of constraince. The basic

writing strokesed the usabili

writing gestumented and ev

In user studiested 1D Hand

mber and efficiility of 1D Han

OWLEDGMEN

work is supportina under Grahua Universityo. 2015130077

D handwritinglish letters. It , such as num One requiremrecognize maperwise, the 1Dition, supportin stroke with mmode selectio

example, on Gre to switch bbetween numbe

learnability, ouarn the 1D strothe strokes aft

for improvemengned learning iure learning. M

mance after lol three experimid not conver

ch, we confirmver, the current

Google Glasstical input and ouch and even

It is also que on other

curved-edgednds, or even 2D

search, we focus interesting to r word level t further impro

ssibility of texned interface:

idea was to into a one-ity issues of

ures. Subsequvaluated 1D Hs, both objecti

dwriting strokeient to use. O

ndwriting.

NTS

ted by the Natant No. 61303y Research Fun71.

g gestures wereis important

mbers, punctument is that thpping between D strokes will bng more characmore characteron to specify

Google Glass, wbetween upperers and punctua

ur research shokes with a fewfter one week.nt. For exampinterface (e.g. Moreover, it is ong-term and

ments, the inpurge due to th

med the feasibt interaction ds: We did notother input senn the embeddinteresting to1D interface

d screen (e.g. D touchscreens

us on 1D handexplore the poinput, i.e. 1

ove the text ent

xt entry on an: the one-dproject two-d

-dimensional sdesigning uniuently, we

Handwriting wiive and subjecte gestures we

Our research v

tural Science F076 and No. nding No. 20

e designed to support

uations and here should 2D strokes be difficult cters means s. One can y different we can use r case and ations

howed that w practices, However,

ple, we can OctoPocus interesting extensive

ut speed of he limited

ility of 1D design may t take full nsors (such

ded motion o test 1D es such as Galaxy S6 s.

dwriting for ossibility of D gesture

try speed.

n emerging imensional imensional space. We istroke 1D

designed, ith Google tive results re easy to

verified the

Foundation 61272230, 151080408


80

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one-dimensional handwriting: inputting letters and words...

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