a smartphone based real time ac5vity monitoring systempalencar/cs846/fall-2016/presentations/... ·...

ASmartphoneBasedRealTimeAc5vityMonitoringSystem

By:ShumeiZhang,PaulMcCullagh,JingZhang,TiezhongYu

Presentedby:JaneHenderson

ASmartphoneBased-RealTimeDailyAc5vityMonitoringSystem 1

Outline

•  Problemandgoalsofthesystem•  Background•  Methodology•  Experiments•  Results•  Takeaways•  Discussion


FALLS

LeadingcauseofinjuryMajorglobalhealthproblem-par5cularlyforelderly3%whofallwillnotreceiveassistancefor20minutes


Howcanwesolvethis?

•  Automa5cmonitoringofdailyac5vi5es

•  Contextawareapplica5ons

•  Pervasivecompu5ng


ProposedSolu5on

SMARTPHONEBASEDACTIVITYMONITORINGSYSTEM

Toclassifymo5onandmo5onlessdailyac5vi5es

anddis5nguishfallsinvarioussitua5ons


Background

•  Howtoclassifyhumanac5vi5esofdailyliving?

•  WearableSensors

•  FeatureExtrac5on

•  Classifica5onofthesefeatures


Howtosensefalls?

•  Largeraccelera5onchangecomparedtonormaldailyac5vi5es

•  Methodsusingonlyaccelerometers?

•  Combineaccelerometerswithothersensors?


ProposedSystem

•  Smartphonebasedfalldetec5onsystem•  Hierarchalrule-basedalgorithm

•  Rule-basedbackwardreasoningalgorithm


Methodology

HTCWildfireSA510ephone


Methodology:DataCollec5on

•  2RawDataSets

•  Samplingfrequency5Hz–80HzCanmisshigh-frequencyvaluesformo5onac5vi5es


DataSensing

•  Accelera5on

•  Accelerometer

•  3DAccelera5on

•  3DOrienta5on


Methodology:PostureClassifica5on

•  Highlevelcontextbasedon:•  (t,id,Ax,Ay,Az,ΔA,θX,θy,θZ)

•  tisthe5mestamp•  idisthecalculatedsamplenumber•  ΔAisthecalculatedaccelera5onchange

•  2typesofac5vi5es:mo5onlessandmo5on


Methodology:Mo5onlessPostures

th1=0.4m/s2

(determinedempiricallyusingcollectedmo5onlessdata)


Methodology:Mo5onlessPostures


Methodology:Mo5onPosturesth2=3.5m/s2

(determinedempiricallyusingcollectedmo5ondata)


Experiments

•  Indoor(realhomeenvironment)•  Real-5me•  Sixhealthypeople(5male,1female,20-52years)

•  Simulated:– Variousfalls– Normaldailyac5vi5es


Experiments

•  Resultsvalidatedagainstnotesbytwoindependentobservers

•  Twoalgorithmsused:•  PosTra(algorithmdescribedinthispaper)+posi5on

•  AccThr


DataSensingandtheSystemInterface

•  AnalyzedResults(t,posture,loca5on,status)

•  Ifcertainfall:fallalert

•  Elseifpossiblefall:musicalertwillsoundandastopbulonwillappear


FallsandFall-LikeAc5vi5es

•  Fall-lying(72)•  Fall-sitTilted(72)•  Normallying(72)•  Bending(36)•  Jumpandsitheavily


Results

•  Normalandabnormaldailyac5vi5esclassifiedusingPosTraandAccThr

•  4aspects:•  (1)Trueposi5ve•  (2)Falsenega5ve•  (3)Truenega5ve•  (4)Falseposi5ve


Results:PosTravs.AccThr


Results:PossibleFallRecogni5on

•  PosTrawilltriggerpossiblefallwhen:

•  Simngperiodof5me<2sbeforenormallying

•  Bending>70°

•  Posturekeepingsit-5ltonachairaqerjumping


Results:NormalLyingLimita5ons


Takeaways

•  Themo5onandmo5onlesspostureswereclassifiedusingahierarchalrule-basedalgorithm•  Trustworthyfordailyac5vitymonitoring

•  Falldetectedwasimplementedbyanalyzingwhetherposturesarenormalorabnormal•  basedontransi5on

•  Musicalertwithastopbulonifpossiblefall


Takeawayscon5nued

•  Thisapproachcan:•  Correctlydetectvariousfallsefficiently

•  Real-5mewithinasmartphone•  Avoidfalseposi5vesandfalsenega5ves

•  Situa5onsaccountedfor:•  Fallquicklyontoground•  Fallslowlyontobed•  Fallsendinginlyingorsit-5lted•  Normallying


Discussion


ReferencesZhang,S.,McCullagh,P.,Nugent,C.,Zheng,H.,Black,N.:Anontologicalframeworkforac5vitymonitoringandreminderreasoninginanassistedenvironment.J.AmbientIntell.Humaniz.Comput.4(2),157–168(2013)Zhang,S.;McCullagh,P.;Zhang,J.;Yu,T.ASmartphoneBasedReal-TimeDailyAc5vityMonitoringSystem.Clust.Comput.17,711–721.(2014)Zhang,S.,McCullagh,P.,Nugent,C.,Zheng,H.:Atheore5calgorithmforfallandmo5onlessdetec5on.In:3rdIEEEInterna5onalConferenceonPervasiveCompu5ngTechnologiesforHealthcare,pp.1–6(2009)ImageReferenceshlp://www.mobile2u.com.pk/Images/Items/HTC_Wildfire_S_image5342.jpghlp://i.stack.imgur.com/gbzQG.png


Addi5onalReadingsCasilari,Eduardo,RafaelLuque,andMaría-JoséMorón."Analysisofandroiddevice-basedsolu5onsforfalldetec5on."Sensors15.8(2015):17827-17894.Fraś,Mariusz,andMikołajBednarz."SimpleRule-BasedHumanAc5vityDetec5onwithUseofMobilePhoneSensors."Informa.onSystemsArchitectureandTechnology:Proceedingsof37thInterna.onalConferenceonInforma.onSystemsArchitectureandTechnology–ISAT2016–PartII.SpringerInterna5onalPublishing,2017.Luque,Rafael,etal."Comparisonandcharacteriza5onofandroid-basedfalldetec5onsystems."Sensors14.10(2014):18543-18574.Yu,Lei,etal."ACompressedSensing-BasedWearableSensorNetworkforQuan5ta5veAssessmentofStrokePa5ents."Sensors16.2(2016):202.Yu,Lei,etal."Aremotequan5ta5veFugl-Meyerassessmentframeworkforstrokepa5entsbasedonwearablesensornetworks."Computermethodsandprogramsinbiomedicine128(2016):100-110.Zhang,Shumei,PaulMcCullagh,andVicCallaghan."Anefficientfeatureselec5onmethodforac5vityclassifica5on."IntelligentEnvironments(IE),2014Interna.onalConferenceon.IEEE,2014.


StrengthsandWeaknessesStrengths Weaknesses•  Provideaprac5calsolu5on

•  Thoroughexplana5onof3Dcoordinatesystem

•  Thoroughexplana5onofcalcula5onsofmo5onlessandmo5onac5vi5es

•  Didnotaccountforsecurity/privacy

concerns

•  Poortransi5onbetweenmethodologyandexperiments

•  Manygramma5calmistakesmadeunderstandingdifficult

•  Limita5onof“simula5ng”falls


FutureWork•  Moreac5vityposturesandfallsitua5onssuchasmovingup/downstairs,cycling,drivingandrunning

•  Tryhighersamplingrates

•  Implementasimilarstudyforsmartwatches/otherwearabletechnology

•  Implementrealworldcasestudy


DiscussionQues5ons•  Howcanwegetaccelerometerdatafromactualfalls,

withoutsimula5on?

•  Whataretheethicalimplica5onsfromusingthistechnology?

•  Doyouthinkthisisaviablesolu5onfortheglobalhealthproblemoffalling?

•  Doyouthinkanotherwearabletechnology(i.e.smartwatches)couldprovidemoreaccuratereadingsforfalls?


a smartphone based real time ac5vity monitoring systempalencar/cs846/fall-2016/presentations/... ·...

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