$3 Recognizer: Simple 3D Gesture Recognition on Mobile Devices

COMMENT
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SUMMARY
The $3 recognizer is a simple, robust 3D gesture recognition method which relies on simple trogonometric and geometric calculations and requires less training samples. Authors feel that increasing number of mobile devices are equipped with 3D accelerometers. This 3D accelorometer data can be used in recognizing gestures.
The gesture data is first resampled so that points are redistributed to be at equal distances. To neutralize rotaional errors gestures, the gesture trace is rotated once along the gestures indicative angle (angle between the first points and the centroid). Lastly input gesture is scaled to fit a normalize cube. The input gesture is compared with all the gestures in the training data set. A scored list of candidate gestures is produced based on the mean square error (MSE). To reduce the occurence of false postitives the authors use a heuristic.

DISCUSSION
An interesting method due to simplicity and ease of implementation. However as mentioned by the authors in another paper describing the algorithm the method cannot recognize gestures in continious motion stream. There has to be an explicit start and end to the gesture.