همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Unlocking individual motor signatures using feature-based clustering of a graphomotor task

نویسندگان :

Zinat Zarandi¹ Amirreza Behmanesh² Mohammad Medhi Ebadzadeh³ Thierry Pozzo⁴

1- INSERM UMR1093-CAPS, UFR des Sciences du Sport, Université Bourgogne Franche-Comté, Dijon, France. 2- Amirkabir University of Technology 3- Amirkabir University of Technology 4- INSERM UMR1093-CAPS, UFR des Sciences du Sport, Université Bourgogne Franche-Comté, Dijon, France.

کلمات کلیدی :

Motor behavior،Fuzzy C-Means clustering،hand-drawing tasks،motor signatures،, feature selection

چکیده :

Understanding individual motor signatures (IMS) is essential for personalized treatment and performance optimization. This study investigates the effectiveness of Fuzzy C-Means (FCM) clustering for identifying individual motor signatures from graphomotor tasks. We analyze various kinematic and geometric features, such as movement duration, velocity, and trajectory length, to reveal which aspects of motor behavior are most effective in distinguishing individuals. The results show that features like length of movement are particularly discriminative, while others, such as beta and velocity, offer weaker clustering outcomes.