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Kinematic specification of dynamics (KSD) states that full-body kinematic patterns of daily activities are reflective of aperson’s plans, goals, and intentions. Furthermore, it has beenshown that observers of those activities are well attuned todifferences between those kinematic patterns. However,despite a substantial body of research on the identification ofintentional motion, it is not yet clear what the essentialkinematic information is required to perceive the intentionfrom the kinematic pattern. Therefore, we analyzed fourdifferent intentional full body motions (sit-to-standtransitions: stand, press-stand, press-sit, and reach-up), todetermine the essential kinematic information thatdifferentiates them. We utilized principal component analysis(PCA), linear mixed models, and hierarchical multinomiallogistic regression to create two predictive regression modelsthat allow us to successfully identify and distinguish the fourintentional motions.