Descripción
|
|
---|---|
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%. | |
Internacional
|
Si |
JCR del ISI
|
Si |
Título de la revista
|
Signal Processing |
ISSN
|
0165-1684 |
Factor de impacto JCR
|
2,063 |
Información de impacto
|
|
Volumen
|
120 |
DOI
|
10.1016/j.sigpro.2015.09.029 |
Número de revista
|
|
Desde la página
|
359 |
Hasta la página
|
372 |
Mes
|
MARZO |
Ranking
|
Journal Rank in Category 66/255; Quartile in Category Q2) |