Unsupervised Learning of Spatio-temporal Primitives of Emotional Gait
Experimental and computational studies suggest that complex motor behavior is based on simpler spatio-temporal primitives. This has been demonstrated by application of dimensionality reduction techniques to signals from electrophysiological and EMG recordings during execution of limb movements. However, the existence of such primitives on the level of kinematics, i.e. the joint trajectories of complex human full-body movements remains less explored. Known blind source separation techniques, e.g. PCA and ICA, tend to extract relatively large numbers of components or source signals from such trajectories that are typically difficult to interpret. For the analysis of emotional human gait patterns, we present a new method for blind source separation that is based on a nonlinear generative model with additional time delays. The resulting model is able to approximate high-dimensional movement trajectories very accurately with very few source components. Combining this method with sparse regression, we identified spatio-temporal primitives for the encoding of individual emotions in gait. We verified that these primitives match features that are important for the perception of emotions from gait in psychophysical studies. This suggests the existence of emotion-specific movement primitives that might be useful for the simulation of emotional behavior in technical applications.
KeywordsBlind Source Separation Movement Primitive Joint Trajectory Blind Source Separation Algorithm Sparse Regression
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