Data driven modeling of head motion towards analysis of behaviors in couple interactions
We propose a data driven approach for modeling head motion behavior in human dyadic interactions, by establishing a structure for unconstrained natural head movement. Using recordings of couples' conversations in real psychotherapy sessions, we first track the head of each subject, compute the head motion and detect active versus non-active intervals. For detected active intervals, we use a sliding window to collect motion sequences. Linear Prediction Coefficients are used to represent the sequence, based on which we train a Gaussian Mixture Model (GMM) such that each mixture would ideally associate with one type of prototypical movement, which we will refer to as a “kineme”. For each complete interaction session, we compute the sum of posterior probabilities of all sequences over the GMM normalized by session length to predict specific “low” versus “high” expert annotated behavior code scores for Acceptance, Blame, Positive and Negative behaviors. We achieved an overall accuracy of about 70% employing these GMMs. This result shows data driven modeling of head motion provides useful information for human behavioral analysis.