Combining acceleration and gyroscope data for motion gesture recognition using classifiers with dimensionality constraints

Abstract

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%.

A gesture-based method for natural interaction in smart spaces

“…The templatebased approaches belong to deterministic method, which measure the similarity of features between unknown gesture signals and templates, and select the template (or cluster classified based on similarity) which best matches the testing pattern as recognition result. The reviewed methods of similarity measurement include Dynamic Time Warping (DTW) [1], Longest Common Subsequence (LCS) [31], Protractor3D [17] and Levenshtein edit distance [11]. On the other hand, the reviewed statistical methods model the motions in a probabilistic manner such as Hidden Markov Models (HMMs) [5,18,37], or are based on probabilistic theory, like Probabilistic Neural Network (PNN) [34] which tries to asymptotically approach the Bayes optical decision surface.…”

“…On the other hand, the reviewed statistical methods model the motions in a probabilistic manner such as Hidden Markov Models (HMMs) [5,18,37], or are based on probabilistic theory, like Probabilistic Neural Network (PNN) [34] which tries to asymptotically approach the Bayes optical decision surface. The authors in [17] argue that the template-based approaches perform well even with few training samples and they are easier to implement and deploy on different types of devices since they do not require specialized libraries. What is more, the probabilistic assumptions on which the statistical approaches are established may not accomplish in practice, for example, the Markov assumption and the stationary assumption set by HMMs.…”

“…(15) which now can be rewrite as Eq. (17). These constraints restrict the slope of the warping path between 0.5 and 2.…”

Design and evaluation of novel gesture-centric interaction models in smart spaces

“…Based on an offline evaluation of the proposed gesture recognition approach, a gesture recognition strategy that first works in user-independent mode and then improves the performance with more availability of training samples, is proposed for Kinectbased applications to essentially eliminate the need of training by new users. Most of the reviewed recognition approaches [21,54,110] are tested with more than one training samples. Some works [96,105,52] are dedicated to approaches with only one training sample.…”

“…Although the vision-based tracking enables the users to do free gestures without cumbersome contact-based devices like data glove, it does have some limitations, such as being prone to be interfered by varying lighting conditions and cluttered background and relatively low sampling rate of normal cameras [21]. The inertial based recognition does not have the [18,109] utilize accelerometer to record the gesture motions while others combine accelerometer with gyroscope [5,54,108,52] and magnetometer [37]. However, the inertial based gesture recognition presents the drawback that the tracking is implicit and it cannot collect position and orientation information.…”