Fall Detection – From Phone to Cloud

-ArchivingPhoneDataonCloudServer

Student:ManvickPaliwal Advisor:Dr.AnneHeeHiongNgu

What Fall Detection is all about?

Introduction

•  ThisIndependentStudyisaboutaFallDetectionSensorApplicationwhichwillhelpdetectifsomeonehasfallendown.Thisapplicationisspeciallydesignedforelderlypeople,butanyonecanuseit.Thewaythisworksiswecollectstreamingsensordatafromasmartwatch.Thesensordatacollectedisthemprocessedtoextractkeyfeaturesrelatedtofallingonasmartphone.ThisprocesseddataisthenfedintoaNaiveBayesmachinelearningalgorithmwhichanalyzesandoutputfallornotfallastheoutcome.

Objective

• Archivingofsensordatatothecloudrobustly.•  Improvingtheaccuracyofthefalldetectionmodelbyretrainingusingarchivedfalsepositivedata.

Raw Data

• Rawdataisstreamedfromthesmartwatchatasamplingfrequencyof32milliseconds.• Westorethisdatainsideafoldernamed“RawData”.• Wewriterawdatatoafilenamecur_timestampunder“RawData”folder• Afterevery5minuteswerenamethisfiletoupload_timestampandcreateanewcur_timestampfileandstartwritingtothatfile.• Upload_timestamparethefileswhicharereadytobeuploaded.

False Positive data

Twowaysinwhichfalsepositivedatacanbestored.•  Storingalltheprocesseddata(Notefficient)•  StoringTimeStamps(MoreEfficient)

False Positive data Continued…

• Westorefalsepositivedatainsideaseparatefoldername“ProcessedData”.•  Filename:TimeStamps.csv•  TimeStampsatthetimefallhappenedislogged

Uploading

• Readingfromalltheupload_timestampfilesandsendingpostrequesttoserver.•  Ifallthedataofaspecificfileisuploadedsuccessfullyasuccesscallbackissentfromtheserver.• Onlyaftersuccesscallbackisreceivedfromtheserverwedeletethespecificfilefromthephone.

Chunking

• Challenges:Ifallsensordataiswrittentosamefileanduploadperiodically,Thefilegetsbiggerandbiggeranditisnotagoodpracticetouploadahugeamountofdataoverhttppostrequest.• AbettersolutionisbasedonuploadingChunks.•  Thesizeofeachchunkisapproximately512kbandeachfilechunkiscreatedevery5minutes(300seconds).

Robustness with 2 phones

• AllthedataisuploadedcorrectlyandinarobustwaywithTwomobilesuploadingdatasimultaneously.•  IfProgramisclosedin-betweenoftheuploadingprocessnoneofthedataislost.•  IftheInternetconnectionislostintheprocessofuploadingofdatathedatawhichisarchivedisdeletedandthedatawhichisnotarchivedissavedsecurelyonclientdevice.•  Eachfileisapproximatelyof512kb.

Processed data

Time Stamp

Processed data script

•  Thisscriptcreatesfalsepositiverawdataonserverusingtimestampsandrawdata•  ThisrawdataisthenpushedtoRscripttocreateprocesseddata.•  Theprocesseddataisthenfinallymergedwithexistingtrainingdatatoretrainthemodel.

Algorithm

•  GetfalsepositiveTimeStamp(byqueryingFalsePositivetable)•  GetSampleIDofeachTimeStampfromSampleMetaDatatable•  Retrieve50sampleIdlessthanthecurrentsampleIdfromSampleMetatData•  Foreachofthe50sampleid,matchthecorrespodingsampleIDtogetalltheaccelermeterdata(Ax,Ay,Az)•  Storethedatainfalse_positive_timestamp.csv•  GobacktoStep2asandrepeatuntillallthefalsepositiveTimeStampisqueried.

Cloud Server

• WehaveaphpserverwithMySQLasthebackend.TherearetwophpscriptswhicharereadytoacceptthedatainJSONformatandinsertitintoMySQLdatabase.•  insertRawData.php•  insertTimeStamps.php

Open Problems

•  SQLite•  TimeStampisnotaccurate.•  Useofclientsidedatabasewillhelpcollectmoreaccuratefalsepositivedata.

•  Systemgetsslow•  Clearprocesseddatafile.•  Circularqueuedatastructurecanbeused.

•  SystemshouldbeRetrainedwithallthetruepositiveandfalsepositivedata• Wehaven’tretrainedthemodelwithsufficientdata•  Ifuserdon’twanttouploadthedatathesystemwillcrasheventually

Conclusion

• Chunking•  Developedprotocoltouploadandarchivedatasensordatafromphonetocloudrobustly.•  Dataisdividedintochunksandthenuploaded.

•  TimeStamps•  Presentedamethodtoreducetheamountofdatatobeuploaded.•  Insteadofuploadingalltheprocesseddataonlytimestampscanbeuploaded.

Thank you -Manvick