Analysis and generation of motion profiles from positive and negative human actions

Human actions convey not only their functional goal, the "what," but also the manner in which they are performed, the "how." The same action may be executed in a Positive or Negative manner depending on the agent's affective state. For example, handing someone an object carefully and politely reflects a positive manner, whereas doing so abruptly and dismissively reflects a negative one. This action modulation, known as Vitality Form (VF), is fundamental to human communication. Despite their importance, VFs remain relatively underexplored. This study investigates the kinematic signatures of Positive and Negative VFs in a pointing action and examines whether they can be reproduced through synthetic motion profiles. To this aim, kinematic data were collected from 40 right-handed participants performing three different pointing actions after recalling pleasant or unpleasant personal academic experiences. Each movement was assigned to either the Positive or Negative VF according to the emotional valence of the recalled experience. Several classifiers were evaluated for predicting VFs from wrist kinematic profiles, achieving a best accuracy of 73.97%. Grad-CAM analysis further provided insights into the kinematic basis of VF discrimination. We then investigated whether the kinematic patterns distinguishing Positive and Negative VFs could also be reproduced in synthetic motion profiles using generative models, achieving a Train-on-Synthetic, Test-on-Real accuracy of 72.50%. Finally, a perceptual study assessed whether the generated velocity profiles conveyed the intended Positive or Negative VFs. Participants identified the corresponding affective category with an accuracy of 75.80%, suggesting that affective information can be transferred to synthetic profiles and retained in a form that remains perceptually recognizable. Overall, this work advances our understanding of how affective states are encoded in human movement, demonstrating that these cues can be modeled, reproduced, and recognized in artificially generated actions. These findings have potential applications in neuroscience and social robotics, enabling artificial agents to recognize human attitudes and adapt their behavior accordingly.