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A novel feature descriptor for gesture classification using smartphone accelerometers

Since gestures are a natural form of human expression, gesture-based interfaces can serve as an alternative interaction modality with numerous aspects to be utilized in human computer interaction. In this paper, we address the issue of finding a compact but effective set of features for a robust gesture recognition, using a single 3-axis accelerometer. A novel feature extraction scheme, that allows the gesture form to be clearly discriminated, is proposed. Fuzzy k-Nearest Neighbour classifier is used for recognition of gestures in transformed feature space. The experiments, conducted on an custom gesture vocabulary, reveal that Histogram of Direction (HoD) descriptor, in conjunction with statistical features, produces a highly competitive performance, in terms of recognition accuracy.