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

Zarandi, Zinat

doi:10.48308/jicse.2025.239593.1084

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

Document Type : Original Article

Author

Zinat Zarandi

INSERM UMR1093-CAPS, UFR des Sciences du Sport, Université Bourgogne Franche-Comté, Dijon, France.

https://doi.org/10.48308/jicse.2025.239593.1084

Abstract

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.
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.

Keywords

Keywords—Motor behavior

Fuzzy C-Means clustering

hand-drawing tasks

motor signatures

feature selection

Subjects