Online Accelerometer Gesture Recognition using Dynamic Time Warping and K-Nearest Neighbors Clustering with Flawed Templates

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dc.contributor.advisorGrootjen, F.A.
dc.contributor.advisorDesain, P.W.M.
dc.contributor.authorKluge, R.M.W.
dc.date.issued2017-08-29
dc.description.abstractIn this thesis we will discuss how we can conduct online accelerometer gesture recognition using Dynamic Time Warping (DTW) and K-Nearest Neighbors (KNN) Clustering. DTW works based on templates. These templates are autonomously extracted from the data streams. However, this algorithm can fail sometimes, resulting in extracted templates not conforming to the template standard. When a template deviates from this standard, it is considered a flawed template. This deviation could either happen by a miscalculated periodicity or wrong starting point. It is investigated if the performance of gesture recognition can be improved when we add flawed gesture templates to the training set. During online classification, it can happen that the template extraction goes faulty and tries to classify a gesture with a flawed template. If these flawed templates are already in the training set, this problem can be tackled and therefore, result in an increased performance. Unfortunately, the difference in mean performance seemed not significant enough (p = 0:208) to verify this hypothesis.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/5211
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleOnline Accelerometer Gesture Recognition using Dynamic Time Warping and K-Nearest Neighbors Clustering with Flawed Templatesen_US
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