Gesture recognition using symbolic aggregate approximation and dynamic time warping on motion data

In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.

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