Kinematic specification of dynamics (KSD) states that full- body kinematic patterns of daily activities are reflective of a person’s plans, goals, and intentions. Furthermore, it has been shown that observers of those activities are well attuned to differences between those kinematic patterns. However, despite a substantial body of research on the identification of intentional motion, it is not yet clear what the essential kinematic information is required to perceive the intention from the kinematic pattern. Therefore, we analyzed four different intentional full body motions (sit-to-stand transitions: stand, press-stand, press-sit, and reach-up), to determine the essential kinematic information that differentiates them. We utilized principal component analysis (PCA), linear mixed models, and hierarchical multinomial logistic regression to create two predictive regression models that allow us to successfully identify and distinguish the four intentional motions.