Abstract
In this study, We implemented a spatiotemporal clustering approach to analyze the outcome of a virtual reality human navigation neuropsychological assessment(the VR Magic Carpet). Our main objective was to establish a clustering of participants using a deep multi-step clustering model on velocity signals extracted during clinical trials. We used a multi-step neural network architecture to analyze the feature extraction and the clustering stage separately. In the feature extraction stage, we adopted a 1D-DCAE autoencoder, and for the clustering, we used a soft temporal clustering layer, a combination of similarity metrics, K-means, and probability. This method enabled us to comprehend, to a certain extent, the clustering results in contrast to the joint architecture we have been using before. We obtained five significant clusters that are associated with specific clinical groups.
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Annaki, I. et al. (2023). Spatiotemporal Clustering of Human Locomotion Neuropsychological Assessment in Virtual Reality Using Multi-step Model. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_98
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