Power-spectral analysis of head motion signal for behavioral modeling in human interaction

We examine whether head motion can be used for predicting human expert's judgments of behavioral characteristics relevant to the couples therapy domain. Specifically we predict “high” or “low” presence of several behavioral characteristics such as “Blame” that are discerned by human experts, through data-driven clustering of the head motion signal based on power-spectral features. We employ the distribution of motion samples in each cluster for behavior judgment prediction. We find clustering horizontal and vertical motion separately is superior to combined clustering in predicting behavior. The performance of gender-specific and gender-independent clustering of head motion is comparable in average while different for each gender. The proposed power-spectral features outperform linear prediction features in average. Using data from a clinical study of distressed couples, we empirically show that the derived clusters quantize head motion into meaningful types that relate to interpretable behavior characteristics. These findings demonstrate the feasibility of inferring behavior characteristics from head motion signals.