2009 · 29 citations · 6 references
EngineeringMachine LearningMeasurementBiometricsWearable TechnologyTime PerceptionBiomedical Signal AnalysisSpeech RecognitionData ScienceUncertainty QuantificationPattern RecognitionHidden Markov ModelTemporal DynamicBiostatisticsStatisticsNonlinear Time SeriesHealth SciencesTemporal Pattern RecognitionComputer ScienceTime VariabilityEntropyPen PressurePen CoordinatesPen Inclination
In this work, we study different combinations of the five time functions captured by a digitizer in presence or not of time variability. To this end, we propose two criteria independent of the classification step: personal entropy, introduced in our previous works and an intra-class variability measure based on dynamic time warping. We confront both criteria to system performance using a hidden Markov model (HMM) and dynamic time warping (DTW). Moreover, we introduce the concept of short-term time variability, proposed on MCYT-100, and long-term time variability studied with BIOMET database. Our experiments clarify conflicting results in the literature and confirm some other: pen inclination angles are very unstable in presence or not of time variability; the only combination which is robust to time variability is that containing only coordinates; finally, pen pressure is not recommended in the long-term context, although it may give better results in terms of performance (according to the classifier used) in the short-term context.
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