Real-Time Motion Classification for Wearable Computing ... · the computing power available in the...
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Real-Time Motion Classification for Wearable Computing Applications Richard W. DeVaul, Steve Dunn December 7, 2001 Abstract In this paper we describe the development of a real-time motion classi- fication system for use in the MIThril wearable computing platform. The purpose of this motion classification system is to make important informa- tion about the user’s state (whether the user is walking, running, standing still, etc.) available to other applications in real-time. We developed a two- layer model that combines a multi-component Gaussian mixture model with Markov models to accurately classify a range of user activity states, includ- ing sitting, walking, biking, and riding the T. 1 Introduction Context awareness, or knowledge about the state of the user, task, or environment derived through non-explicit user input, is widely seen as a key to reducing the complexity of human-computer interaction tasks for portable and wearable com- puting applications. Context awareness requires statistical inference techniques to turn noisy sensor data into useful contextual information. Precisely what con- texts are useful depends on the application, but a list of generally useful contexts include the time of day, the user’s location, and user’s activity state. Time of day is easily obtained with a real-time clock, and outdoor location is comparatively easy to determine through GPS (indoor location is another mat- ter, but may be partly addressed through the use of active tags 1 or the use of a 1 ... such as the Crystal tag system based on the Squirt IR active tags (http://www.media. mit.edu/~rich/) or the Locust system developed by Thad Starner, et. all. 1
Transcript of Real-Time Motion Classification for Wearable Computing ... · the computing power available in the...
Real-Time Motion Classificationfor WearableComputingApplications
RichardW. DeVaul,SteveDunn
December7, 2001
Abstract
In this paperwe describethedevelopmentof a real-timemotionclassi-fication systemfor usein the MIThril wearablecomputingplatform. Thepurposeof this motionclassificationsystemis to make importantinforma-tion aboutthe user’s state(whetherthe useris walking, running,standingstill, etc.) availableto otherapplicationsin real-time.We developeda two-layermodelthatcombinesamulti-componentGaussianmixturemodelwithMarkov modelsto accuratelyclassifya rangeof useractivity states,includ-ing sitting,walking,biking, andriding theT.
1 Introduction
Context awareness,or knowledgeaboutthestateof theuser, task,or environmentderived throughnon-explicit userinput, is widely seenasa key to reducingthecomplexity of human-computerinteractiontasksfor portableandwearablecom-puting applications.Context awarenessrequiresstatisticalinferencetechniquesto turn noisysensordatainto usefulcontextual information. Preciselywhatcon-texts areusefuldependson theapplication,but a list of generallyusefulcontextsincludethetimeof day, theuser’s location,anduser’s activity state.
Time of day is easilyobtainedwith a real-timeclock, andoutdoorlocationis comparatively easyto determinethroughGPS(indoor locationis anothermat-ter, but may be partly addressedthroughthe useof active tags1or the useof a
1... suchastheCrystaltagsystembasedontheSquirtIR activetags(http://www.media.mit.edu/~rich/) or theLocustsystemdevelopedby ThadStarner, et. all.
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triangulation-basedroom positioningsystem2The user’s physicalactivity state,suchaswalking, sitting, riding theT, etc.,is morecomplicatedto determineandalsoquiteimportant,sincesomeonewhois walkingis likely to havelessavailableattentionthansomeonewho is stationary, andsomeonewho is riding a bicycle islikely to have evenlessthansomeonewho is walking. Thekindsof informationthat theuserneedsunderthesethreeconditionsis alsolikely to bedifferent. Webecameinterestedin theproblemof determiningtheuser’s physicalactivity statein real-timeusingahigh-precisionthree-axisaccelerometerwornonthetorsoandthecomputingpoweravailablein theMIThril wearablecomputingplatform.
2 Methodology
Themethodologyusedfor this projectis asfollows:
1. Gatherannotateddatafrom real-world activities.
2. Analyzethisdataandfeatureselection.
3. Developasuitablegenerativeor discriminativemodel.
4. Implementthemodelon thewearableandanalyzetheresults.
2.1 Data Collection
Thefirst stepof theprojectwasto gatherlabeleddatawhile engagingin a rangeof typicaldaily activities,suchasstanding,walking,riding theT, riding abicycle,etc. This involved putting on the wearable,running the accelerometerloggingprogramanda text loggingprogram,andgoingaboutdaily activities.
2.1.1 Sensor Hardware
Wedesignedamicrocontroller-basedaccelerometersensorusingtheADXL202JEpartfrom AnalogDevices.This two-axisaccelerometeris accurateto betterthan2% over a
���G range.Two of theseaccelerometersaremountedat right angles
to eachother, resultingin a four-axissensorwith two redundantaxes.2such as the LCS Cricket project, LCS Cricket Projecthttp://nms.lcs.mit.edu/
projects/cricket.
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2.1.2 Sensor Software
Sensorlogging codepolled the accelerometersensorapproximately47 timesasecond,readingall four axes.Eachsamplewastimestampedwith Unix timeplusmillisecondsandloggedin a text file.
2.1.3 Annotation System
Thedatawasannotatedin realtimeby typing linesof text on theTwiddlerchord-ing keyboard(a commerciallyavailableone-handedkey entry device). Whenaline of text wascompleted(whenthe“Enter” key waspressed)thetext wastimestampedwith Unix time plusmillisecondsandloggedin a text file. This annota-tion schemeallows for precisematchingof labelswith data. Labelsrecordedinthis way includedsinglecharacterabbreviation for commonstates,suchas“w”for startingto walk and“s” for stopping,aswell aslongerfree-formannotation,suchas“ damn,still breathinghard- i’m out of shape.”
2.1.4 Data Acquisition
Over thecourseof a coupleof weeks,Oneexperimenter(Rich DeVaul) worethewearableon adaily basisandgenerateda numberof datasetsof varyingsizeandquality. Oncethe hardwareandsoftwareglitcheswere ironedout, this left twolarge (order1/2 hour) datasetsof uniform samplingrate,goodannotation,andinterestingsubjectmatter. Oneof thesedatasetswasgatheredlatein theeveningwhenRich wasliterally runningfor thelastT home.TheotherwasgatheredonemorningwhenRich bikedin to work. Anotherextremelyinterestingdatasetthatincludeda bike crashhadto bediscardedbecausetheaccelerometerdatawasn’tproperlytimestampedandthesamplingratecouldn’t bedetermined.3.
3 Analysis
We startedwith a four-componentsignalvectorcomposedof unsignedbytes(0-255) for eachaxis,sampledat rateof about47.8Hz, andirregularly spacedtextlabels. Both the labelsandaccelerometersamplesweretime-stampedwith mil-lisecondresolutionandstoredin flat text files. We hadtwo usefuldatasetswith
3Ironically thecrashwascausedby Richansweringhiscell-phone- preciselythetypeof inter-actionwe aretrying to obviatethroughbettercontext sensingin wearablecommunicationsapps.BothRich andthewearablewerefine.
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a combinedtotal of 198,855samplesfor 4,156secondsof dataandabout110meaningfullabels.
A pre-processingstepaveragedthetwo redundantcomponentsof thefour-axisaccelerometervectorto produceathree-vector. To simplify therepresentationandreducedimensionalityweconvertedtheaccelerationvectorto ascalarmagnitude.Experimentingwith spectrogramsin Matlabsuggestedthata power spectrumofthemagnitudemightbeagoodfeaturefor classifyingmotionstate.To reducethecomputationalburdenonthewearablewechoseacomparativelysmall64elementFFT window (spanning1.34seconds)with 32 samplesof overlap,producingone32 elementpower-spectrumevery 0.67 seconds. Becauseour data includedalargeDC offset thatcausednumericalstability problemswe threw away theDCcomponent,leaving a31dimensionalfeaturevector.
4 Modeling
4.1 BIC, EM and Gaussian Mixture Model
Sinceour goal was to develop the simplestpossiblemulti-classactivity model(and we had known-working Gaussianmixture-modelcodein Matlab) we de-cidedto useBasianInformationCriterion andEM to clusterthe data. Much toour surprise,this worked fairly well; Statingwith our first dataset,BIC choseafive-componentmodelwith onecomponentclearly associatedwith runningandanotherwith walking.
Sinceour goal is to implementreal-timeclassificationon the wearable,thenext stepwasto implementfeature-extraction in C codethat we could compileon the wearable.We choseFFTW4 for the basisof our power-spectrumfeatureextractioncodebecauseit is bothfastandfree.Unfortunately, thetransformspro-ducedby FFTW differ from thoseproducedusingMatlab’s FFT function. That,andabugin some“known working” Matlabproblem-setcode5 causedusnosmallamountof confusionuntil we realizedwhatwasgoingon.
With our FFTW-generatedfeatures,we re-ranBIC on thefull labeleddatasetandproducedaneight-componentmodethatalsohadstrongclustersfor walkingandrunning,but other labeledactivity satessuchassitting still or riding the T
4“The FastestFourierTransformin theWest,” – seeFFTWhomepagehttp://fftw.org/for moreinformation.
5Thereis aparenthesisationerrorin multigaussian.mthatcausesincorrectdensityevaluation.
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appearedto havehigh-orderdynamicsthatthemodelcouldnotcapture.After implementingthemulti-Gaussianmodelevaluationonthewearableand
verifying that the classconditionalprobabilitieswerethesameasin our Matlabimplementation,weturnedto theproblemof bettercapturingthedynamicsof ourdata.
4.2 Combining Models: Gaussian Mixtures and Markov Chains
Oncewe realizedtherewasdynamicstructurein our datathatour modelwasn’tcapturingwehadseveralchoices.
1. We could choosea larger window for our FFT in the hopesthat the dy-namicfeaturesof interestwouldfall within thelargerwindow andrerunourBIC/EM modelselectionprocess.
2. Wecouldkeepourshorterfeaturevectorandattemptto build anHMM thatcapturedthedynamicfeaturesof interest.
3. We could use the output of our existing model and constructa Markovmodelon topof it thatwouldcapturethedynamics.
The first choiceseemedinfeasibledue to the large computationalrequirementsit would impose. The secondapproachseemedto be the most principled, butwouldhaverequiredadditionaltheory(HMMs with continuousobservablestates)andcodeto implement. The third approach,while perhapsnot optimal in per-formance,requiredlittle additionalcodebeyondwhatwe hadalreadywritten orusedfor problemsets,couldbeimplementedeasilyin C,andwasthustheobviouschoice.
Theresultaresix first-orderMarkov modelsrunningontheoutputof theeight-componentGaussianmixturemodel.TheMarkov modelsrepresentthefollowingstates:
1. Garbage- a modeltrainedon a sectionof thecombineddatasetwe don’tcareabout. SeeFigure1 for sourceaccelerometerdataandspectrogramandFigure2. for theGaussiancomponentposteriorsandMarkov modelclassificationresultsfor this data.
2. Sitting - a modeltrainedon dataof Rich waiting for theT. SeeFigure3 forthesourceaccelerometerdataandspectrogramandFigure4 for theGaus-siancomponentposteriorsandMarkov modelclassificationresultsfor thisdata.
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Figure1: “Garbage”classtrainingdata
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3. Walking - amodeltrainedona longstretchof Richwalking from theDavissquareT stopto hishouseon KidderAve. SeeFigures5 and6
4. Running- a modeltrainedon two shortsectionsof Rich runningfrom theMediaLab to theKendallT stop.SeeFigures7 and8..
5. Riding theT - a modeltrainedon a long sectionof Rich riding theT fromKendallto Davis. SeeFigures9 and10.
6. Riding a bicycle - themostcomplicatedmodel.A modeltrainedon a longsectionof Rich biking from his homein Davis Squareto the Media Lab,including variouspauses,stops,andchangesin intensitycharacteristicofbikecommutingin Boston.Figures11 andf12.
Thefigurescomparetheresultsof theGaussianclassconditionalassignmentsandthe Markov Model classificationrunning on thoseassignmentsas inputs. TheMarkov Model classificationresultsare the result of 20-symbolinputs, whichrepresentsa delayof 13.4secondsfor the endof the window to catchup to thebeginningof anew activity state.
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Figure6: Classificationresultsfor “Walking” data.
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The Gaussianclassconditionalassignmentsdo a good,thoughnoisy job, ofclassifyingthecomparatively unambiguousactively statesof walking vs. runningvs. sitting,anddosoquitequickly; adelayof asingleFFTwindow (1.34seconds)is all thatis necessary. However, thegraphsindicatethatall of thesestatesareeas-ily confusedwith themorecomplex activity of biking, andsitting still vs. ridingtheT areessentiallyindistinguishableevenat theone-countdynamicslevel.
Thefirst-orderMarkov modelclassifierdoesa muchbetterjob of classifyingthefiveactivitiesof interestwith a20-symbolinput. Althoughtheinherentimpre-cision in the labelingprocessmakesquantifyingthe“true” activity stateof eachsampledifficult, the resultsof classificationusingthefirst-orderMarkov modelsappearssurprisinglygood,especiallyconsideringthecomplexity of thedataandthe arbitrariness(from the point of view of the Markov model)of the Gaussianmixturemodelparameters.
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5 Implementation
Thegoalof thisprojectis areal-timeimplementationof amotionclassifiersystemrunningontheMIThril wearablecomputingplatform.This requiresthatthestepsof sensing,featureextraction. modelevaluation,andclassificationtake placeinreal-timein an environmentof limited computingresources.Needlessto say,runningMatlabonMIThril is right out.
A the time of this writing, we have implementedandtestedevery partof theclassificationprocessexcepttheMarkov modelevaluation.ThefeatureextractionandGaussianclass-conditionalposteriorcodeproducesidentical resultson thewearableas on the desktopunderMatlab, running in real-timeon a 200MIPSStrongArmwith 32MB of ram. We haveevery reasonto believe thattheadditionof first-orderMarkov modelevaluationwill bepossibleaswell.
6 Conclusions
Thegoalof developingandimplementingareal-timemotionclassificationsystemfor the MIThril wearableis largely accomplished.We speculatethat the hybridGaussianmixturemodel/Markov modelwe developedmaynot beasaccurateasamonolithicHMM, but maypresentcertainadvantagesaswell. For instance,theclassconditionalposteriorscouldbeusedto providea rapid“is theusermoving”classificationfor applicationsthatareinterestedin only coarsemotiondata,whileapplicationsrequiringmoreprecisioncouldusetheresultsof theMarkov modelclassification.Further, it shouldbepossibleto train new Markov models,perhapsof higherorderor for differentactivity states,without retrainingthe underlyingmixturemodel.
Oneconcernis over fitting; We usedall of our labeleddataastraining datasowe haveno way of empiricallydemonstratingthemodel’s generalization.Ouruseof BIC in modelselectionprovidessomeassurancethatourGaussianmixturemodelhasareasonableexpectationgeneralization,but wehavenosuchassurancewith our choiceof Markov modelparameters.Only moredataandtestingwilltell.
Futuredirectionsincludecompletingtheimplementationof theMarkov modelevaluationonthewearableanddevelopingtheinfrastructureto supportothertypesof modelsor classifierssuchasHMMs or SVMs.
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Figure13: Full DataSetwith Classification
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ubtu
rngst
opon
kid
der
star
ting
stop
to fi
x hi
p st
rap
stop
to fi
x hi
p st
rap
stop
stop
star
tm
ount
bik
ego
t bik
est
opst
eps
door
open
ing
gara
ge d
oor,
etc
20
sec
wal
kdo
orbo
qqbo
ttom
star
lsw
alk
and
back
on
−−
5 s
ec a
gota
ke o
ff ba
ckpa
ckno
t!pu
t on
hip
pack
stop
wal
kst
opw
alk
oncput
on
pick
up
pack
wal
king
stop
get u
psi
k d
wn
now
on
put m
ithril
on
stop
wal
kdo
orw
alk
top
stai
rsst
art w
alki
ngst
opst
op
wal
kst
opw
alk
stop
wal
kdo
orst
airs
fron
t gat
ew
alk
esca
lato
rw
alk
clim
b es
cala
tor
esca
lato
r
wal
kga
tew
alk
stai
rs u
p −
slo
ww
alk
stan
d −
− le
avin
g tr
ain
trai
n ju
st s
tart
edst
art
sitti
ngw
alki
ngst
andi
ng u
pta
ke b
ag o
ffsi
t
stop
wal
kbo
ttom
stai
rs d
own
run
door
at m
edic
alru
nst
opst
art d
own
elev
ator
paus
ew
alk
Ann
otat
ed G
auss
ian
Com
pone
nt P
oste
rior
Cla
ssifi
catio
n P
lot,
0.82
to 4
156.
10 s
econ
ds
mixture component number
500
1000
1500
2000
2500
3000
3500
4000
2468
at la
bs
sggs
light
light
sgo
ln
man
squ
are
light
sno
sto
p at
pan
ini
no s
top
at h
ighl
and
gogolig
ht a
t elm
and
mos
slan
ds
sgo stu
rnscr
ubcr
ubtu
rngst
opon
kid
der
star
ting
stop
to fi
x hi
p st
rap
stop
to fi
x hi
p st
rap
stop
stop
star
tm
ount
bik
ego
t bik
est
opst
eps
door
open
ing
gara
ge d
oor,
etc
20
sec
wal
kdo
orbo
qqbo
ttom
star
lsw
alk
and
back
on
−−
5 s
ec a
gota
ke o
ff ba
ckpa
ckno
t!pu
t on
hip
pack
stop
wal
kst
opw
alk
oncput
on
pick
up
pack
wal
king
stop
get u
psi
k d
wn
now
on
put m
ithril
on
stop
wal
kdo
orw
alk
top
stai
rsst
art w
alki
ngst
opst
op
wal
kst
opw
alk
stop
wal
kdo
orst
airs
fron
t gat
ew
alk
esca
lato
rw
alk
clim
b es
cala
tor
esca
lato
r
wal
kga
tew
alk
stai
rs u
p −
slo
ww
alk
stan
d −
− le
avin
g tr
ain
trai
n ju
st s
tart
edst
art
sitti
ngw
alki
ngst
andi
ng u
pta
ke b
ag o
ffsi
t
stop
wal
kbo
ttom
stai
rs d
own
run
door
at m
edic
alru
nst
opst
art d
own
elev
ator
paus
ew
alk
Ann
otat
ed M
arko
v C
lass
ifica
tion
Plo
t, 0.
82 to
415
6.10
sec
onds
markov Class number
500
1000
1500
2000
2500
3000
3500
4000
246
14
