Motivated by the addition of gyroscopes to a large number of new smart phones, we study the effects of combining accelerometer and gyroscope data on the recognition rate of motion gesture recognizers with dimensionality constraints. Using a large data set of motion gestures we analyze results for the following algorithms: Protractor3D, Dynamic Time Warping (DTW) and Regularized Logistic Regression (LR). We chose to study these algorithms because they are relatively easy to implement, thus well suited for rapid prototyping or early deployment during prototyping stages. For use in our analysis, we contribute a method to extend Protractor3D to work with the 6D data obtained by combining accelerometer and gyroscope data. Our results show that combining accelerometer and gyroscope data is beneficial also for algorithms with dimensionality constraints and improves the gesture recognition rate on our data set by up to 4%.
Reference REF presented a study on the effectiveness of combining acceleration and gyroscope data on mobile devices for gesture recognition using classifiers with dimensionality constraints.
Combining acceleration and gyroscope data for motion gesture recognition using classifiers with dimensionality constraints
IUI '13
Computer Science