Does a cycle-based segmentation improve accelerometer-based biometric gait recognition?

When machine learning algorithms are used for accelerometer-based biometric gait recognition, the general approach is to divide the data into segments of a fixed time-length, extract features from the segments and use these feature vectors to train the classifiers and authenticate the subjects. In this research we enhance the segmentation by a cycle extraction method. A gait cycle contains data of two steps, and our technique assures that each segment contains the same amount of steps. These cycle-based segments are input to the feature extraction process. We apply Hidden Markov Models (HMMs) for classification and compare the results to previous ones obtained using segments of a fixed time-length. We show that the fixed-length segmentation is the better approach as it achieves similar error rates while requiring a lower computational effort.