Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Abstract

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

“…Recently, deep learning models, based on various variants of neural networks, such as CNN [ 26 ], RNN [ 27 ] or GNN [ 28 ], are also experiencing a renaissance. Depending on the area and specificity of applications, very complex approaches are used, often utilising integration and combination of many techniques, which is particularly visible in case of interdisciplinary problems occurring, for example, in such selected fields as medicine [ 29 , 30 ], education [ 31 , 32 ], metrology [ 33 , 34 , 35 ], biometrics [ 36 , 37 ], learning of motor activities [ 38 , 39 ] or gesture recognition [ 40 , 41 ].…”

Effectiveness of a Head Movement Interface for Steering a Vehicle in a Virtual Reality Driving Simulation

“…Regarding head movements, Hachaj et al [14] studied the recognition and classification of seven head gestures, which included clockwise/counterclockwise rotation and nodding, by using machine learning techniques. In addition, previous studies have investigated controlling a robot [15] and a wheelchair by the movement of the head for people with tetraplegia [16][17][18].…”

EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies

“…DTW allows for a flexible definition of cumulative point-to-point distances to optimize alignment performance for different datasets. Because of this flexibility, DTW is widely used in speech recognition under varying speaking speeds [1], gesture recognition [2,3], and time series clustering [4].…”