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In this paper, we propose a novel unsupervised learning approach for spatio-temporal pattern classification. We use a spike timing neural network with axonal conductance delays to learn the structure of spatio-temporal patterns from a small set of training samples and then use this network for classifying unseen patterns. The method also enables early classification of patterns, before they are completely observed. To transform a spatio-temporal pattern into a suitable form for the spiking network, we create a mapping process that transforms it into a spike train that can be used to train the network through spike-timing-dependent plasticity. Based on the trained network, we build models of the training samples as strings of characters in which each character represents a set of neurons that fire at a particular time step, in response to the pattern. For classification, we compute the longest common subsequence between the model strings corresponding to the input and training samples and choose the class of the training sample with highest similarity. This method is evaluated on a handwritten digits dataset with spatio-temporal information. We show that this method is robustly detecting the correct class early on. Comparison with one unsupervised and eight other supervised approaches shows that our proposed approach could compete or has better performance rather than all of them. Further analysis show that even for misclassified samples, our system can detect the correct class among the top three class labels. [ABSTRACT FROM AUTHOR]

We have evaluated four locomotion interfaces, namely natural walking (NW), omnidirectional treadmill (OT), walk-in-place (WiP), and joystick (JS). In this within-group study, an avoidance movement task with a virtual character was performed by all participants for each examined interface. Our study considers that natural walking is the most realistic method for navigating in a virtual environment and explores the differences across the examined locomotion interfaces by collecting avoidance movement measurements (clearance distance, trajectory length, and trajectory curvature) and self-reported subjective ratings (simulation sickness, usefulness, satisfaction, ease of use, and task load). The results suggest that, despite the fact that the avoidance movement measurements of the WiP, JS, and NW interfaces share similarities, they, more often than not, differ from the measurements of the OT interface, which makes the OT interface unable to provide precise avoidance movement data for our participants. Moreover, the OT interface was rated lower by participants in terms of learning, usability, efficacy, satisfaction, physical demand, and effort. Our study shows that NW, OT, WiP, and JS as locomotion interfaces present several benefits and drawbacks concerning their application in avoidance movement behavior tasks with a virtual character. [ABSTRACT FROM AUTHOR]