Online Accelerometer Gesture Recognition using Dynamic Time Warping and K-Nearest Neighbors Clustering with Flawed Templates
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2017-08-29
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en
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Abstract
In 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.
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Faculteit der Sociale Wetenschappen