Extraction of spatio-temporal primitives of emotional body expressions

Experimental and computational studies suggest that complex motor behavior is based on simpler spatio-temporal primitives, or synergies. This has been demonstrated by application of dimensionality reduction techniques to signals obtained by electrophysiological and EMG recordings during the execution of limb movements. However, the existence of spatio-temporal primitives on the level of the joint angle trajectories of complex full-body movements remains less explored. Known blind source separation techniques, like PCA and ICA, tend to extract relatively large numbers of sources from such trajectories that are typically difficult to interpret. For the example of emotional human gait patterns, we present a new non-linear source separation technique that treats temporal delays of signals in an efficient manner. The method allows to approximate high-dimensional movement trajectories very accurately based on a small number of learned spatio-temporal primitives or source signals. It is demonstrated that the new method is significantly more accurate than other common techniques. Combining this method with sparse multivariate regression, we identified spatio-temporal primitives that are specific for different emotions in gait. The extracted emotion-specific features match closely features that have been shown to be critical for the perception of emotions from gait pattern in visual psychophysics studies. This suggests the existence of emotion-specific motor primitives in human gait.