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Descripción
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| This paperproposestheadaptationofwell-knownstrategiessuccessfullyusedinspeech processing: MelFrequencyCepstralCoefficients (MFCCs)andPerceptualLinearPrediction (PLP) coefficients. AdditionallycharacteristicslikeRASTA filtering ordeltacoefficients are also consideredandevaluatedforinertialsignalprocessing.Theseadaptationshavebeen incorporated intoaHumanActivityRecognitionandSegmentation(HARS)systembased on HiddenMarkovModels(HMMs)forrecognizingandsegmentingsixdifferentphysical activities: walking,walking?upstairs, walking-downstairs,sitting,standingandlying. All experimentshavebeendoneusingapubliclyavailabledatasetnamedUCIHuman ActivityRecognitionUsingSmartphones,whichincludesseveralsessionswithphysical activity sequencesfrom30volunteers.Thisdatasethasbeenrandomlydividedintosix subsets forperformingasix-foldcrossvalidationprocedure.Foreveryexperiment, averagevaluesfromthesix-foldcross-validationprocedureareshown. The resultspresentedinthispaperovercomesignificantly baselineerrorrates,con- stitutingarelevantcontributioninthe field. AdaptedMFCCandPLPcoefficients improve human activityrecognitionandsegmentationaccuracieswhilereducingfeaturevector size considerably.RASTA-filtering anddeltacoefficients contributesignificantly toreduce the segmentationerrorrateobtainingthebestresults:anActivitySegmentationError Rate lowerthan0.5%. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Signal Processing |
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ISSN
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0165-1684 |
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Factor de impacto JCR
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2,063 |
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Información de impacto
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Volumen
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120 |
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DOI
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10.1016/j.sigpro.2015.09.029 |
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Número de revista
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Desde la página
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359 |
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Hasta la página
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372 |
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Mes
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MARZO |
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Ranking
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Journal Rank in Category 66/255; Quartile in Category Q2) |