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

Sven Kratz

Deutsche Telekom Laboratories, TU Berlin Ernst-Reuter-Platz 7 10587 Berlin, Germany sven.kratz@telekom.de

Michael Rohs

Deutsche Telekom Laboratories, TU Berlin Ernst-Reuter-Platz 7 10587 Berlin, Germany michael.rohs@telekom.de

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Summary:

The article presents the $3 gesture recognizer algorithm which is designed to be implemented quickly, is device independent, does not require any special toolkits or frameworks, in stead relies solely on trigonometric and geometric calculations. With the inclusion of 3D accelerometers in many mobile devices the range of motion is no longer restricted by the size of the device. This work is an extension of Wobbrock et al.’s $1 2D algorithm, resulting in a 3D recognizer..

The gesture data is obtained by collecting a series of acceleration values which are the differences between consecutive measurements. This set is then compared to the gesture library and a set of matching scores determined by the heuristic which are then used to determine if a gesture has been correctly identified.

As with 1$ resampling is performed to equalize the number of points between the candidate gesture and the members of the library. As with $1 a rotation is applied along an axis between the first point and the centroid of the gesture. The size is then normalized by scaling the gesture to fit in a set cube space. Mean Squared Error is used to match candidate gestures with those in the library. Rotational differences are compensated for with Golden Section Search.

There is a heuristic in $3 which leads to a reduction in false positives over $1. The scoring heuristic is able to deal with the reduction in precision of the accelerometer data as compared with a tablet entry.

Results:

Considerable recognition in false positives over $1. However, an actual figure is not stated.

Discussion:

The extension to $1 from 2D to 3D is indeed of interest. The main extension seems to be attributes to a scoring heuristic which is used to determine whether a library match has been made. The difference in application to less precise accelerometer data is also interesting.

However, the absence of any quantitative data to support the claimed improvements over $1 is concerning.