Shahid Beheshti University Journal of Innovations in Computer Science and Engineering (JICSE) 2981-2135 3 1 2025 06 01 Unlocking individual motor signatures using feature-based clustering of a graphomotor task 43 47 106267 10.48308/jicse.2025.239593.1084 EN Zinat Zarandi INSERM UMR1093-CAPS, UFR des Sciences du Sport, Université Bourgogne Franche-Comté, Dijon, France. Journal Article 2025 09 22 Abstract—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. <br />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. https://jicse.sbu.ac.ir/article_106267_7791995e2015364845929d63d832d4b9.pdf