The $3 recognizer: simple 3D gesture recognition on mobile devices

Authors:

Sven Kratz

Michael Rohs

Summary:

Goal: build a simple 3D gesture recognition system - simple to implement and require less training data

A gesture recognizer was build for 3D gestures extended Wobbrock's 1$ recognizer for 2D touch gestures. It is easy to implement, requires as less as 5 samples for good recognition result. The system uses raw acceleration data. The change in acceleration is calculated. A sequence of acceleration delta is used to represent a gesture trace. A class library contains a list of gesture traces. The user entered gesture is compared to the class libraries to generate a score table. A heuristic is then applied to recognize the gesture.Resampling, rotation to indicative angle and rescaling are used to normalize the user performed gesture. MSE at best angle if found for each class and a score table is generated. To reduce false positives, a heuristics is used. A threshold value is set. The score table is sorted based on the scores. A heuristic to select class is defined based on threshold. If the threshold dont hold the gesture is recognized as unclassified.

Discussion:
Very similar to the $1 recognizer. easy to implement and easy to train. Providing accuracy data would have been more compelling in defending the classifier. introducing "unclassified" tag is an improvement over the $1 recognizer.

Comment: Franck