On the development of a computer-based handwriting assessment tool to objectively quantify handwriting proficiency in children

jou rn a l h o m e pa g e:ww w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b

On

the

development

of

a

computer-based

handwriting

assessment

tool

to

objectively

quantify

handwriting

proficiency

in

children

Tiago

H.

Falk

a,b,c

,

Cynthia

Tam

b

,

Heidi

Schellnus

a,b,c

,

Tom

Chau

a,b,c,∗

a_{BloorviewResearchInstitute,Toronto,Canada}

b_{Holland-BloorviewKidsRehabilitationHospital,Toronto,Canada}

c_{InstituteofBiomaterialsandBiomedicalEngineering,UniversityofToronto,Canada}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received2February2010 Receivedinrevisedform 3December2010 Accepted10December2010 Keywords: Handwriting MHA Gripforce Digitizingtablet

a

b

s

t

r

a

c

t

StandardizedwritingassessmentssuchastheMinnesotaHandwritingAssessment(MHA) caninforminterventionsforhandwritingdifficulties,whichareprevalentamong school-agedchildren.However,thesetestsusuallyinvolvethelaborioustaskofsubjectivelyrating thelegibilityofthewrittenproduct,precludingtheirpracticaluseinsomeclinicaland edu-cationalsettings.Thisstudydescribesaportablecomputer-basedhandwritingassessment tooltoobjectivelymeasureMHAqualityscoresandtodetecthandwritingdifficultiesin children.Severalmeasuresareproposedbasedonspatial,temporal,andgripforce measure-mentsobtainedfromacustom-builthandwritinginstrument.Thirty-fivefirstandsecond gradestudentsparticipatedinthestudy,nineofwhomexhibitedhandwritingdifficulties. StudentsperformedtheMHAtestandweresubjectivelyscoredbasedonspeedand hand-writingqualityusingfiveprimitives:legibility,form,alignment,size,andspace.Severalspatial parametersareshowntocorrelatesignificantly(p<0.001)withsubjectivescoresobtained foralignment,size,space,andform.Gripforceandtemporalmeasures,inturn,serveas usefulindicatorsofhandwritinglegibilityandspeed,respectively.Usingonlysizeandspace parameters,promisingdiscriminationbetweenproficientandnon-proficienthandwriting canbeachieved.

©2010ElsevierIrelandLtd.Allrightsreserved.

1.

Introduction

Handwriting,definedhereasmanuscriptwritingconsisting onlyofindividualprinted letters,isanimportantlifeskill. Handwriting has been recognized by the child and youth editionoftheInternationalClassificationofFunctioning, Dis-ability and Health (ICF) as a necessary skill for learning and applyingknowledge[1].Itisknown thatdifficulties in handwriting canleadto delayin thedevelopment of writ-ten language [2–4], diminished emotional well-being and

∗ _{Correspondingauthorat:Holland-BloorviewKidsRehabilitationHospital,Toronto,Canada.Tel.:+14164256220.}

E-mailaddress:[email protected](T.Chau).

socialfunctioning[5,6]aswellasreducedself-confidenceand personal relationships [7]. Toprevent the adverse effectof handwritingdifficultiesonchilddevelopment,earlyreferral fortherapyisrecommended[8],preferablyduringthesecond halfofseniorkindergarten[9].

Themostcommonlyreporteddifficultieswithhandwriting areslowspeedandreducedquality[10–12].Teachersindicate, however,thatreadabilityofthewrittenproductisofprimary concern[13].Numeroushandwritingassessmentsare avail-able today [14] and serve as diagnostic toolsto determine whether or not the student iswriting atrates and quality

0169-2607/$–seefrontmatter©2010ElsevierIrelandLtd.Allrightsreserved. doi:10.1016/j.cmpb.2010.12.010

levelsthatarecomparabletotheirpeers.Despitescoring cri-teriabeingavailabletoguidetherapists,existinghandwriting qualityassessmenttestsarelaborious[15]anddependon sub-jectivejudgmentofthewrittenproduct[14].

Inlightofthesignificanteconomicandhumanresource invested in remediation and the risk of delaying child developmentthrough inappropriateintervention, clinicians acknowledgetheneedtobettercomprehendtheunderlying processesofhandwritingforinterventionplanning[16].Asa consequence,thereisgrowinginterestinquantitative

determi-nantsofproficienthandwriting.Asattestedbyrecentresearch,

computerizedinstrumentationcanplayakeyrolein develop-ingsuchmetrics[15,17–21].

To date, computerized instrumentation has consisted mostlyofdigitizingtabletsconnectedtoapersonalcomputer and have focused on measuringkinematics (e.g., position, angle,velocity,andaccelerationofthepen),temporal param-eters,andthedownward(normal)force exertedbythepen on the writing surface. Onlyafew studies have measured gripforces,i.e., theforces exertedradially onthe barrel of thewritingutensil,forassessmentofhandwriting function

[17,22,23].Earlierworkshowedthatgripforcepatternsdiffered betweenchildrenandadults[22],whilemorerecentworkhas suggestedthatgripstrengthcanbeusedtodistinguish able-bodiedchildrenwithnoknownhandwritingdifficultiesfrom childrenwithspatichemiplegiccerebralpalsyandfinemotor difficulties[17,23].Tothebestofourknowledge,nopublished studyhaslookedatgripforcepatternstoquantifyhandwriting difficultiesinable-bodiedchildren.

Inthispaper,acustomhandwritinginstrumentation sys-tem is described, built from off-the-shelf hardware that simultaneouslymeasuresgripforces,normalforces,x–y posi-tions on the tablet, and timing information. We derived severalobjective parametersfrom thesemeasures thatmay serveascorrelates ofthe subjectiveMinnesota Handwriting Assessment(MHA)[24,25]ratescoreandhandwriting qual-ityprimitivesconsistingoflegibility,form,alignment,size,and

space. The overarching goal of this study is to investigate

thecorrelationbetweentheproposedbiomechanical param-etersandsubjectivehandwriting rateandqualityscores. A secondary goal is to gauge the potential ofdiscriminating between proficient and non-proficient handwriting on the basisoftheproposedquantitativemeasures.Ultimately,itis hopedthatcomputer-basedanalysisofwrittenproductivity, includingautomatedanalysisofgripforcepatterns,willallow forfast,reliableandobjectiveassessmentofhandwriting pro-ficiencyinchildren.

2.

Methods

2.1. Participants

Participantsincluded16first-gradestudentsand19 second-gradestudents(ages84.9±7.3months)recruitedfromalocal communityschool.Participantswereidentifiedasproficient or non-proficientwriters usingthe Minnesota Handwriting AssessmenttestdescribedinSection2.3.Usingthistest,nine studentsweredeemednon-proficient,fourofwhichwerefirst graders.Thestudywasapprovedbytheresearchethicsboards

ofthehospitalandparticipatingelementaryschool.All partic-ipantsfreelyconsentedtothestudy.

2.2. Instruments

A Wacom 9in.×12in. Intuos3 digital tablet and an instru-mented wireless pen were used in this study. A custom graphite nib for the Wacom pen was developed to better approximate the feel of a pencil and to provide the stu-dents with the conventional pen-to-paper experience they are accustomed to in their educational environment. The pen was furtherinstrumentedwith aTekScan model9811 pressure sensor arrayon its barrel (4 elementsin azimuth by8elementsalongtheaxisofpen).Thesensorsmeasured the force appliedradially to thebarrel bythe user’shand, i.e.,thegripforce.Thetotalweightoftheinstrumentedpen was 19.7g with a diameter of 16.9mm. This is somewhat heavier and thicker than a newly sharpened Dixon D308 primary school pencil (weight=11.1g, diameter=10.3mm) but physically similar to a child-sized Crayola marker (weight=12.6g, diameter=14.9mm) and to previously-reported instrumented pens (e.g., the one described in

[17]).

Customelectronicsweredevelopedtomultiplexthe sen-sorsignalsfortransmissionoverathin,light,compliantcable toaNationalInstrumentsdataacquisitioncard,asdepicted inFig. 1(a).Customsoftwarewasdevelopedtorecord time stamps, x and y positions, and verticalpressures from the Wacontabletatasamplerateof75Hz.Gripforceateachof the32sensorsonthepenbarrelwasalsorecordedbya cus-tomsoftwareatasamplerateof20Hz.Boththetabletandgrip sensor data acquisition programs were time-synchronized. Children’shandmovementswerealsorecordedonvideoin ordertodetectanyeventsthatmayhavecausedprolonged pausesduringthewritingtask.

2.3. MinnesotaHandwritingAssessment

TheMinnesotaHandwritingAssessment(MHA)testrequires studentstocopywordsfromaprintedstimulussheeteither inmanuscriptorD’Nealianstyleprint.Thewordsarefromthe sentence“thequickbrownfoxjumpedoverlazydogs”,which areshortinlengthandcontainalllettersofthealphabet.The wordsare presentedinmixedordertoreducethememory advantageofbetterreaders[25];wordorderisdisplayedin

Fig.1(b).Inthisstudy,themanuscriptversionoftheMHAwas used.

The MHA isused due to its validity [14,26], as well as its short administration time of 2.5min. The test subjec-tivelyquantifiesfivequalityaspectsofstudents’handwriting

– legibility, form, alignment, size, and space – as well as a

rate score. Alignment, size, and space are judged on the basisofrulermeasurement;legibilityandform,ontheother hand,requiresubjectivejudgement.Atthebeginningofthe rating process,atotal of34 points are givento each cate-gory(one pointper letter).Duringrating, thetotal number of errors in each category are subtracted from this total. The ratescore is determined based on the number of let-ters completed in the 2.5min. The scores are then used to classify students as “performing like peers”,

“perform-Fig.1–Custom-builthandwritinginstrumentation:(a)blockdiagramand(b)actualsetupoftheWacomtabletwiththe MinnesotaHandwritingAssessmenttestsheet.

ingsomewhatbelowtheirpeers,”or“performingwellbelow theirpeers”.

2.4. Experimentalprocedure

All therapistsinvolved inthe study were requiredto com-plete the training protocol specified by the MHA before the test was administered. The Wacom tablet was placed on a desk or table at a comfortable height for the child and MHA test sheets were fastened with tape to the top of the tablet, as depicted in Fig. 1(b). Participants were given 2.5min to copy all the words inthe page and were instructedtocopy letterswiththe same sizeasthe exam-ple and to attempt to use good handwriting. Scoring was performedbytwoexperiencedratersandinterrater reliabil-ity correlationsrangedfrom 0.91 (legibility)to 0.98(space). It was observed that the scores obtained with the instru-mentedpenwereinlinewiththosereportedintheliterature for average 1st and 2nd graders (e.g., [24–26]) using a conventional pencil, suggesting minimal effect of writing implement.

2.5. Tabletdataanalysis

Toautomatethescoringprocess,severalparametersare com-puted from spatial metrics obtained from the tablet data. First,individualcharactersaredetectedandaboundingbox isplacedaround theletter,asdepicted inFig. 2. The “cen-treofmass”(CM)oftheletterenclosedbytheboundingbox iscomputed usingMathwork’sMatlab® _{functionregionprops}

and isrepresented with the symbol“×” inthe figure. The widthand heightofboundingboxesare computed fortwo classesofletters:tall/descending(e.g.,t,q,y)andsmall(e.g., x,o,r)letters.TheseparametersarerepresentedasWtandHt,

orWs andHs,respectively,inFig.2(a).Distancesdbetween

lettersarequantifiedbythehorizontaldistancebetweentwo neighbouringCMs.DistancesbetweenwordsD,inturn,are quantifiedbythehorizontaldistancebetweenthelastletter inawordandthefirstletterofthenextword.Lastly,theangles

betweentwoneighbouringCMsinawordarecomputedin

degrees.Using thesemetrics,variousgeometricand spatial parameters arecomputedand usedascorrelatesoffourof thefiveMHAqualityprimitives,asdetailedinthesubsections tofollow.

2.5.1. Form

Formisaqualityprimitivethatdescribesletterquality, partic-ularlycapturinginstancesofexaggeratedsmallletters(e.g., letter‘s’inFig.2(b))andcompressedtall/descendingletters (e.g.,letter‘k’inFig.2(b)).Usingthewidthsandheightsofthe detectedboundingboxes,weproposetousethreecorrelates ofletterform,namely,theaveragesmallandtallletteraspect ratios (ARs andARt respectively),and theratio of small-to-tallletteraspectratios(STAR).Thethreemeasuresaregiven by AR_s= 1 21 21

i=1 Ws,i Hs,i , (1) ARt= 1 13 13

i=1 Wt,i Ht,i, (2) STAR= ARs ARt (3)

where the values“21” and “13” in the denominators (and upper summation limits) in the first two equations cor-respond to the total number of small and tall letters, respectively.TheSTARmeasureshouldideallybecloseto2, as bothletter types should haveequalwidths but tall let-ters should have double the height ofsmall letters (since studentswere supposedtowritewithinthereferencelines depictedbyFig.1(b)).Significantdeviationsfromthis thresh-old indicate that the child is not consistent with their form. For the purpose of this study, both ascending (e.g., ‘t’)and descending(‘g’)lettersare consideredtobetall let-ters.

Fig.2–Automaticsegmentationforletterdetectionfor(a)proficientand(b)non-proficientwriter.The“×”symbol representsthe“centreofmass”ofthelettersurroundedbytheboundingbox.

2.5.2. Alignment

Thealignmentqualityprimitiveidentifiesletters placedfar aboveorbelowthereferencelinesavailableintheMHAscore sheets(seeFig.1(b)andtheword‘fox’inFig.2(b)).Usingthe anglescomputedbetweensuccessiveCMs,weproposetouse i)averageanglebetweensuccessiveCMswithinaword(), averagedoveralleightwords(i.e,atotalof26angles)andii) standarddeviationoftheangles(A)ascorrelatesof

align-ment.Parametersarecomputedas:

= 1 26 26

i=1 i (4) A=

1 25 26

i=1 (i−). (5) 2.5.3. Size

Thesizequalityprimitivemeasuresthedistancesofthe let-terstothe mid,upper(forascenders), andlower reference lines(fordescenders)availableintheMHAscoresheets.Using the detected bounding boxes, we propose to use i) coeffi-cientofvariation(CVH)ofbothtallandsmallletterheights H={Hs,Ht}andii)tall-to-smallletterheightratio(TSHR)as quantitativedeterminantsofsize.Parametersarecomputed asfollows: CVH=

1 33 34

i=1 (Hi−H) H ×100%, (6) TSHR= Ht Hs , (7) where Hs= 1 21 21

i=1 Hs,i, Ht= 1 13 13

i=1 Ht,i,and H=₃₄1 34

i=1 Hi,

whereagain,the values“21”and“13”inthedenominators anduppersummationlimitscorrespondtothetotalnumber of small and tall letters,respectively, and “34”is the total numberofletters.

2.5.4. Space

Thespacequalityprimitiveidentifiesexcessiveorinsufficient spacesbetweensuccessivelettersand/orwords(e.g., exces-sivespacebetween‘quickdogs’inFig.2(b)).Usinginter-letter andinter-worddistancescomputedfromthetabletdata,we proposetousei)theaveragenormalizedinter-letterdistance (NILD) and ii) the average normalized inter-word distance (NIWD)asestimatorsofthespaceprimitive.Theseparameters aregivenby:

NILD= 1 26 26

i=1 di STAR , (8)

NIWD= 1 6 6

i=1 Di STAR , (9)

wherethevalues“26”and“6”correspondtothenumberof spacesbetweenlettersandbetweenwords,respectively.

2.6. Gripforceanalysis

Gripforceisdefinedhereastheforcemagnitudesappliedtoall 32gripsensorsonthepenandsummedtoresultinanoverall gripforcemeasureF(t),giveninNewtons,fortimeinstancet. Whiletheinstrumentedacquisitionsystemprovided discrete-timedata,weusecontinuous-timenotationforconvenience. Gripforcemeasurementsfromeachofthe32gripsensorson thepenwerecalibratedusingvendordata.Itishypothesized thatmetrics derivedfromtemporal gripforce patternscan providecorrelatesofqualityprimitivesformandlegibilityas biomechanicalprocesseslikelyplayasignificantfactorinpoor handwriting(e.g.,increasedstiffness)[27].Pastresearchwith childrenwithcerebralpalsyhasalreadydemonstratedthat severalgripforceparametersmayserveasindicatorsof non-proficientprinting[17].

Inordertoobservethevariabilityofthegripforcesover time, the root-mean-square (Frms) valueof the overall grip

forcetemporalserieswascomputedforconsecutiveT-second segments,i.e., Frms(n)=

1 T

lT (l−1)T F(t)2dt. (10)

Here,Tisempiricallysetto2s,thus75Frmssegmentswere

availableinthe2.5mindatarecordingsession.Inparticular, wecomputedthestandarddeviation(rms)oftheFrmstemporal

series,givenby:

rms=

1 74 75

n=1 (Frms(n)−Frms), (11) where Frms= 1 75 75

n=1 Frms(n). 2.7. Temporalanalysis

Verticalpressuresandtimingrecordedbythedigitizingtablet areusedtocomputeaverageper-strokedurations(TS)andthe standarddeviationofper-strokedurations(T).Eachstroke

duration TSi is computed as the time duration where

ver-tical pressures were detected by the instrumentation (i.e., “on-paper”times). Motivatedbypreviousstudies [15,18,19], total in-air time (IA) is also computed and explored as a potential correlate of the MHA rate score. To account for unexpectedpauses(e.g.,childscratchingarm),videoswere analyzedwheneverin-airdurationexceeded2s.Thetemporal

parametersarecomputedas: TS= 1 Ns Ns

i=1 TS_i, T=

1 Ns−1 Ns

i=1 (TS_i−TS), IA= Nia

i=1 IA_i,

whereNs andNiaarethetotalnumberofstrokesandin-air

periods,respectively.

2.8. Automateddiscriminationofproficient handwriting

According to MHA guidelines,students whose scores were in the lower 25th percentile of the grade-level samples were considered to be performing “below their peers,” thus suggesting non-proficient writing [24]. Using this classificationstrategy,nineofthe35participantswere con-sidered to be non-proficient writers, four of which were in grade one and five in grade two. Careful analysis of each individual MHA quality primitive suggests that for both grades legibility, size, and space are the three most influential factors in characterizing non-proficient writing, as shown in Fig. 3(a). Primitives size and space, however, exhibit the most discrimination power, as illustrated by

Fig. 3(b). Asaconsequence,weinvestigatethe use of size-and space-dependent parameters CVH, TSHR, NILD, and

NIWD forautomateddiscrimination ofproficient handwrit-ing.

3.

Results

Pearsoncorrelationcoefficientswere computedbetweenall proposedmeasuresandtheMHArateandfivesubjective qual-ityscores.Tables1–3reportsignificantcorrelations(p<0.001) forallparticipants, aswell asforparticipantsseparatedby gradeandbyhandwritingproficiency,respectively.The aver-age small and average tall letter aspect ratios, as well as the in-air time parameter are omitted from the tables as theyresultedininsignificantcorrelationswithMHArateand qualityscores.Forcorrelationsseparatedbygradeand writ-ing proficiency (Tables 2and3,respectively), Fisher’sz-test witha90%confidencelevelisusedtoexploreifdifferences in correlationsare statisticallysignificant. When separated by grade, parameters with correlations that differ signif-icantly (p<0.1, identified by an asterisk in the table) are relatedtolettersizeandspacing.Moreover,whenseparated bywritingproficiency,significantdifferencesincorrelations are observedonlyforparameters relatedto alignment.For second gradersand for non-proficient writers, the compu-tationofcorrelationcoefficientsforthetemporalmeasures wasnotpossible(indicatedbytheterm “NaN,”nota num-ber,inthetable)asallstudentsobtainedthesamefullrate score.

For automated discrimination of proficient handwrit-ing, it is observed that proposed parameters rms, NILD,

2224 2628 3032 34 0 10 20 30 22 24 26 28 30 32 34 MHA legibility MHA size MHA space Proficient Non−proficient 0 5 10 15 20 25 30 35 20 25 30 35 MHA size MHA space Proficient Non−proficient

a

b

Fig.3–ScatterplotofMHAqualityprimitives:(a)legibility,size,andspaceand(b)sizeversusspace.Symbols“

о

”and“×” correspondtoproficientandnon-proficientwriters,respectively.Notethatsomeproficientwritersobtainedequalspaceand sizeMHAscores,thusoverlapintheplots.Thesolidlinerepresentsthebestlinethatseparatesthetwoclassesofwriters.

Table1–CorrelationsobtainedbetweentheproposedmeasuresandtheMHAratescoreandthefivehandwritingquality primitivesforall35participants.

Analysis Proposedmeasure Overall,n=35

Rate Leg. Form Align Size Space

Temporal TS −0.66 T −0.67 Gripforce rms 0.73 0.68 Tablet STAR −0.83 −0.81 ␴A −0.89 CVH 0.77 TSHR 0.88 NILD −0.87 NIWD −0.74

Table2–CorrelationsobtainedbetweentheproposedmeasuresandtheMHAratescoreandthefivehandwritingquality primitivesseparatedbystudentgrade.Theterm“NaN”indicatesthatcomputationofthecorrelationcoefficientwasnot possiblesinceallstudentsobtainedthesamesubjectivescore.Anasteriskindicatesthatdifferencesincorrelation coefficientsbetween1stand2ndgraderswerestatisticallysignificant(p<0.1)ata90%confidencelevelusingFisher’s z-test.

Analysis Proposedmeasure 1stGrade,n=16 2ndGrade,n=19

Rate Leg. Form Align Size Space Rate Leg. Form Align Size Space

TS −0.66 NaN Temporal T −0.74 NaN Gripforce rms 0.74 0.70 0.70 0.65 Tablet STAR −0.87 −0.80 −0.81 −0.76 ␴A −0.92 −0.83 CVH 0.89* 0.73* TSHR 0.92 0.90 NILD −0.92* −0.80* NIWD −0.87 −0.74

and TSHR are most influential, as illustrated by Fig. 4(a). Size- and space-dependent parameters NILD and TSHR, in turn, are shown to convey the most discriminatory power and, as depicted by Fig. 4(b), can correctly identify all non-proficient writers with a simple linear classi-fication strategy based on linear discriminant analysis

[28].

4.

Discussion

4.1. Beyondscreening:identificationofspecificissues withrateandquality

Literature hassupported the use ofdigitized toolsto con-ductorassistwithhandwritingassessments[15,17–21].Most

Table3–CorrelationsobtainedbetweentheproposedmeasuresandtheMHAratescoreandthefivehandwritingquality primitivesseparatedbywritingproficiency.Theterm“NaN”indicatesthatcomputationofthecorrelationcoefficientwas notpossiblesinceallstudentsobtainedthesamesubjectivescore.Anasteriskindicatesthatdifferencesincorrelation coefficientsbetween(non-)proficientwriterswerestatisticallysignificant(p<0.1)ata90%confidencelevelusingFisher’s z-test.

Analysis Proposedmeasure Proficient,n=26 Proficient,n=9

Rate Leg. Form Align Size Space Rate Leg. Form Align Size Space

TS −0.70 NaN Temporal T −0.72 NaN Gripforce rms −0.72 0.68 0.46 0.66 0.59 Tablet STAR −0.85 −0.74 −0.65* −0.88* ␴A −0.81 −0.90 CVH 0.63 0.73 TSHR 0.79 0.78 NILD −0.72 −0.83 NIWD −0.64 −0.29 0 5 10 1.2 1.4 1.6 1.8 2 10 15 20 25 30 35 40 TSHR σrms NILD Proficient Non−proficient 1 1.2 1.4 1.6 1.8 2 10 15 20 25 30 35 40 TSHR NILD Proficient Non−proficient

a

b

Fig.4–Scatterplotof(a)rms,TSHR,andNILDand(b)NILDversusTSHR.Symbols“

о

”and“×”correspondtoproficientand

non-proficientwriters,respectively.Thesolidlinerepresentsthebestlinethatseparatesthetwoclasses.

digitized tools, however, have only focused on screening fornon-proficientwriting,and havenotidentifiedthe spe-cific difficulties in written productivity which contribute to poor handwriting, suchas poor letter form. This study hastaken a first steptowards the automaticidentification ofspecifichandwriting difficulties.In particular,significant correlationsbetweenquantitativeparametersandexpert rat-ings of rate and quality (legibility, form, alignment, size and spacing) have been uncovered. These findings sug-gest that it may be possible to comprehensively assess handwriting rate and quality objectively, using an instru-mented writing utensil and digitizing tablet. For example, bymeasuringgrip force duringa handwriting task,it may be possible to infer legibility, given the high correlation between these two variables (r=0.73, p<10−6_{). Likewise,}

the standard deviation of the angle between the centre of mass ofconsecutive words would assist at pinpointing alignment issues, as these two variables are also strongly anti-correlated(r=−0.89,p<10−13_{).Thestandarddeviationof}

strokedurationswouldprovideinformationabout handwrit-ing rate(r=−0.67,p<10−5_{),whilesmall-to-tallletteraspect}

ratioswouldrevealissuesofletterform(r=−0.83,p<10−9_).

Finally, tall-to-small letter height ratios may identify size discrepancies (r=0.88,p<10−12_{), whereas normalized}

inter-letter distanceswould uncover irregular spacing(r=−0.87,

p<10−12_).

Taken together,thesestrongcorrelationssuggestthatin thefuture,handwritingrateandqualitymightbequicklyand objectively assessedfrom ashort writing sampleusing an instrumented utensiland digitizing tablet.These objective assessments could complement clinical acumen and sub-jective ratings. In fact, treatment decisions may be better informedwiththeadditionofbiomechanicalquantitieswhich evaluate specific aspects of writing quality. Conventional handwritingassessmentsliketheMHAoftenrequire special-ized testmaterialsandtrainedraters,thereforedemanding non-trivialcommitmentsoftimeandhumanresources–a luxuryoftennotavailableinmostschoolsettings.Thefindings

reported herein support the development of an economi-calandobjectivecomputerizedhandwritingassessmentthat retainsthecriticalandtargetedinformationaboutrateand quality.

4.2. Grade-andfunction-dependentcorrelatesofrate andquality

Comparinggradesoneandtwo,significantdifferenceswere foundinthecorrelationsbetweensubjectivescoresand quan-titativeparametersrelatingtosizeandspace(i.e.,coefficient ofvariationofletter heightsand averagenormalized inter-letterdistances).Itisobserved,however,thatthecorrelations inquestionareallhigh(|r|>0.73)andareconsistentin direc-tion between grades. This finding suggests that while the patterns ofcorrelationare similar across grades,the letter heightandinter-letterdistanceparametersaremorestrongly associatedwithsizeandspacequalityprimitives,respectively, foryoungerwriters.Giventhathandwritingisarelativelynew skillattheagesunderconsideration,itisnotsurprisingthat theremaybeadevelopmentalprofiletocertainquantitative measures,due inpart toprogressivemotormaturation, as arguedin[29].Inturn,thissuggeststhatgrade-levelor age-specificassessmentsmaybenecessary,asalsoarguedin[24]. Comparingproficientandnon-proficientwriting,theonly significant difference in correlation was observed between subjectivescoresandtheaveragewithin-wordangles;for non-proficientwriting,astrongeranti-correlationwasobserved. Asthewrittenlettersdepartfurtherfromthereferencelines, theanglebetweensuccessivecentresofmasswithinaword increases,butmoresoinnon-proficientwriting.Thisfinding suggeststhatwhilethedirectionofcorrelationisconsistent acrossproficientandnon-proficientwriting,certain quanti-tativeparametersmaybemoresensitiveindetectingspecific qualitydeficits,afindingalsoarguedin[30].

4.3. Theneedforgripforcemeasurement

An additional important finding of this study is the fact thattabletinformationaloneisshowntoprovideinsufficient informationforobjectivemeasurementofthelegibility qual-ityprimitive,asall spatial-and temporal-related measures resultedininsignificantcorrelationswithsubjectivescores. Tothisend,gripforceanalysis,madepossiblebythe custom-builtinstrumentationdescribedinSection2.2,isrequired.The proposedgripforcemeasure–standarddeviationofgripforce root-mean-squarevalues–describesthechild’sgripstrategy anditsdynamicsovertime.Significantpositivecorrelations attainedwithMHAlegibilityscoressuggestthatmoredynamic grasps(higherrms)are relatedtoimprovedlegibility. Such

quantitativefindingisinlinewithclinicalacumen[31]and warrantsfurtherinvestigation.

4.4. Automateddiscriminationbetweenproficientand non-proficientwriting

Interestingly,in-air time, a parameter previously proposed asbeingacutelydiscriminatorybetweenproficientand non-proficient Hebrew writing [19,32], exhibited no significant correlationswithMHAratescores(p=0.53). Hebrewwriting

consists of disjoint letters mostly formed in a counter-clockwise direction,written from right toleft,with vowels formedbyadding dots orlines belowor aboveletters [33]. SincetheamountofdotsandcrossesusedintheMHAwere significantlylowerfromthedotsandcrossesusedin[19,32], wesuspectthesedifferencesmayhaveleadtothediscrepant in-airtimeresultsbetweenthepresentEnglishandprevious Hebrewwritingstudies.

Ontheotherhand,averagestrokedurationexhibited sig-nificant differences (t-test, p=0.03) between proficient and non-proficient writing,with the latterrequiring moretime per-stroke. This finding resonates with clinical observa-tion that children withhandwriting difficulties oftenwrite slower and corroborate literaturereportsofthe same (e.g.,

[34]). Parameters relating to letter size and space con-veyedthemostdiscriminatoryinformationbetweenproficient and non-proficient writing. As emphasized by Fig. 4, non-proficient writingiscategorized mostlybylargeinter-letter distances and the inability to discriminate between small and tall/descendingletter sizes.Similar findingshave been reported previouslywherespatial inaccuracieswere shown to significantly contribute to poor handwriting in children

[35–37].

4.5. Limitations

Theresultspresentedhereinare basedon asingle admin-istrationoftheMHAonadigitizingtablet.Therepeatability of the quantitative parameters would need to be investi-gatedbeforetheycouldserveasanadjuncttoconventional subjective assessments. The correlation findings described herein are based exclusively on the critical grades during which handwritingquality rapidlydevelops[38,39], but the broaderdevelopmentalprofiles oftheidentifiedparameters deservefurtherinvestigationbywayofalargercross-sectional study. The reported discrimination between proficient and non-proficientwritingisbasedlargelyonthegraphical demar-cation of a two-dimensional feature space using a simple linear classifieratpresent.Thesystematicidentificationof keydiagnostic parameterswouldnecessitatemorerigorous quantificationofthediscriminatorypowerofdifferent combi-nationsofquantitativeparameters.

5.

Conclusion

We have described and validated an innovative computer-based handwriting assessment toolto objectively quantify handwriting proficiency in children. Ten parameters are proposedbasedonspatialmetricscomputedfromanx–y dig-itizingtabletandfromgripforcepatternsmeasuredfroma custom-built peninstrumented with32 force sensors. The proposedparametersarestatisticallyassociatedwiththefive handwriting quality primitives available in the Minnesota HandwritingAssessmenttestaswellaswritingspeed.Asa consequence,automaticidentificationofspecificdifficulties inwrittenproductivity maybemadepossiblewiththe use ofdigitizedhandwritingtools,thusovercomingtheneedfor time-consuming,resource-intensivesubjectiveassessments. Moreover,theproposedparameterobtainedfromgripforce

measurements suggests that writers who possess a more staticgripforcepatternattainlowerlegibilityscores.Lastly, theproposedparametersrelatingtolettersizeandspacing demonstratepotentialfordiscriminatingbetweenproficient andnon-proficienthandwritinginchildren.

r

e

f

e

r

e

n

c

e

s

[1] WorldHealthOrganization,Internationalclassificationof functioning,disabilityandhealth,childrenandyouth version,2005.

[2] V.W.Berninger,R.Abbott,D.Zook,S.Ogier,Z.Lemos-Britton, R.Brooksher,Earlyinterventionforreadingdisabilities: teachingthealphabetprincipleinaconnectionist framework,JournalofLearningDisabilities32(6)(1999) 491–503.

[3] S.Graham,Handwritingandspellingskillsinwriting development,LearningDisabilityQuarterly22(1999)78–98. [4] S.Graham,K.R.Harris,Theroleofself-regulationand

transcriptionskillsinwritingandwritingdevelopment, EducationalPsychologist35(1)(2000)3–12.

[5] H.Cornhill,J.Case-Smith,Factorsthatrelatetogoodand poorhandwriting,AmericanJournalofOccupational Therapy50(1996)732–739.

[6] C.Morrison,P.Metzger,P.Pratt,Play,in:J.Case-Smith,J. O’Brien(Eds.),OccupationalTherapyforChildren, Mosby/Elsevier,1996,pp.504–523.

[7] R.Sassoon,Dealingwithadulthandwritingproblems, HandwritingReview11(1997)69–74.

[8] D.Marr,S.Cermak,Consistencyofhandwritinginearly elementarystudents,AmericanJournalofOccupational Therapy57(2)(2003)161–167.

[9] L.Edwards,Writinginstructioninkindergarten:examining anemergingareaofresearchforchildrenwithwritingand readingdifficulties,JournalofLearningDisabilities36(2) (2003)136–148.

[10] S.J.Amundson,M.Weil,Prewritingandhandwritingskills, in:OccupationalTherapyforChildren,Mosby,1996,pp. 545–570.

[11] R.Karlsdottir,T.Stefansson,Problemsindeveloping functionalhandwriting,PerceptualMotorSkills94(2)(2002) 623–662.

[12] N.Weintraub,S.Graham,Writinglegiblyandquickly:a studyofchildren’sabilitytoadjusttheirhandwritingto meetclassroomdemands,LearningDisabilitiesResearch andPractice13(1998)146–152.

[13] S.L.Hammerschmidt,P.Sudsawad,Teachers’surveyon problemswithhandwriting:referral,evaluation,and outcomes,AmericanJournalofOccupationalTherapy58 (2004)185–192.

[14] K.PFeder,A.Majnemer,Children’shandwritingevaluation toolsandtheirpsychometricproperties,Physicaland OccupationalTherapyinPediatrics23(2003)65–84.

[15] S.Rosenblum,A.Dvorkin,P.Weiss,Automaticsegmentation asatoolforexaminingthehandwritingprocessofchildren withdysgraphicandproficienthandwriting,Human MovementScience25(2006)608–621.

[16] C.J.Daly,G.T.Kelly,A.Krauss,Relationshipbetween visual-motorintegrationandhandwritingskillsofchildren inkindergarten:amodifiedreplicationstudy,American JournalofOccupationalTherapy57(4)(2003)459–462. [17] T.Chau,J.Ji,C.Tam,H.Schwellnus,Anovelinstrumentfor

quantifyinggripactivityduringhandwriting,Archivesof PhysicalMedicineandRehabilitation87(2006)1542–1547. [18] S.Rosenblum,D.Chevion,P.Weiss,Usingdatavisualization

andsignalprocessingtocharacterizethehandwriting

process,DevelopmentalNeurorehabilitation9(2006) 404–417.

[19] S.Rosenblum,S.Parush,P.Weiss,Computerizedtemporal handwritingcharacteristicsofproficientandpoor handwriters,AmericanJournalofOccupationalTherapy96 (2003)933–954.

[20] B.Smits-Engelsman,G.vanGalen,Dysgraphiainchildren: lastingpsychomotordeficiencyortransientdevelopmental delay?JournalofExperimentalChildPsychology67(1997) 164–184.

[21] S.Athenes,I.Sallagoity,P.Zanone,J.Albaret,Evaluatingthe coordinationdynamicsofhandwriting,HumanMovement Science23(2004)621–641.

[22] V.Herrick,W.Otto,Pressureonpointandbarrelofawriting instrument,JournalofExperimentalEducation30(1961) 215–230.

[23] D.Fernandes,T.Chau,Fractaldimensionofpacingandgrip forceinhandwritingstrokeproduction,Journalof

Biomechanics41(2008)40–46.

[24] J.Reisman,MinnesotaHandwritingAssessment,The PsychologicalCorporation,1999.

[25] J.Reisman,Developmentandreliabilityoftheresearch versionoftheMinnesotahandwritingtest,Physicaland OccupationalTherapyinPediatrics13(2)(1993)41–55. [26] K.Roston,J.Hinojosa,H.Kaplan,UsingtheMinnesota HandwritingAssessmentandhandwritingchecklistin screeningfirstandsecondgraders’handwritinglegibility, JournalofOccupationalTherapy,Schools&Early Intervention1(2)(2008)100–115.

[27] B.Smits-Engelsman,A.Niemeijer,G.vanGalen,Finemotor deficienciesinchildrendiagnosedasdcdbasedonpoor grapho-motorability,HumanMovementScience20(2001) 161–182.

[28] P.Lachenbruch,M.Goldstein,Discriminantanalysis, Biometrics(1979)69–85.

[29] S.Rueckriegel,F.Blankenburg,R.Burghardt,S.Ehrlich,G. Henze,R.Mergl,P.HernáizDriever,Influenceofageand movementcomplexityonkinematichandmovement parametersinchildhoodandadolescence,International JournalofDevelopmentalNeuroscience26(7)(2008) 655–663.

[30] S.Graham,M.Struck,J.Santoro,V.Berninger,Dimensionsof goodandpoorhandwritinglegibilityinfirstandsecond graders:motorprograms,visual-spatialarrangement,and letterformationparametersetting,Developmental Neuropsychology29(1)(2006)43–60.

[31] SAmundson,M.Weil,Handwritingevaluationand interventionintheschoolsetting,in:Developmentofhand skillsinthechild,TheAmericanOccupationalTherapy Association,Inc.,1992,63–78.

[32] S.Rosenblum,S.Parush,P.Weiss,Theinairphenomenon: temporalandspatialcorrelatesofthehandwritingprocess, PerceptualandMotorskills96(3)(2003)933–954.

[33] A.Yochman,S.Parush,Differencesinhebrewhandwriting skillsbetweenisraelichildreninsecondandthirdgrade, PhysicalandOccupationalTherapyinPediatrics18(3)(1998) 53–65.

[34] S.Rosenblum,M.Livneh-Zirinski,Handwritingprocessand productcharacteristicsofchildrendiagnosedwith developmentalcoordinationdisorder,HumanMovement Science27(2)(2008)200–214.

[35] D.Johnson,J.Carlisle,Astudyofhandwritinginwritten storiesofnormalandlearningdisabledchildren,Reading andWriting8(1996)45–59.

[36] B.Smits-Engelman,G.vanGalen,S.Portier,Psychomotor aspectsofpoorhandwritinginchildren,in:Contemporary IssuesintheForensic,DevelopmentalandNeurological AspectsofHandwriting,AssociationofForensicDocument Examiners,1994,pp.17–44.

[37] M.Simner,Printingerrorsinkindergartenandthe predictionofacademicperformance,JournalofLearning Disabilities15(3)(1982)155.

[38] K.Feder,A.Majnemer,Handwritingdevelopment,

competency,andintervention,DevelopmentalMedicineand ChildNeurology49(4)(2007)312–317.

[39] SCahill,Wheredoeshandwritingfitin?Strategiesto supportacademicachievement,InterventioninSchooland Clinic44(4)(2009)223.