Harnessing Machine Learning for interpersonal physical alignment

This work presents a novel way to determine interpersonal physical synchrony state by inspecting hands' postures obtained from a unique 3D depth camera device named Leap-Motion Controller. Several ML methods are utilized such as SVM, shallow feed-forward ANN and XGBoot. We show that even a simple ANN can outperform XgBoost in simple classification tasks.