Software for the analysis of reach-to-grasp and transport movements: applications in cognitive and neurophysiological research

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Abstract

Aim. To present Kinematic 4, a software tool designed for automated analysis of reach-to-grasp and object transport movements using data obtained from motion capture systems.

Materials and methods. The software was developed in Python and implements algorithms for automatic detection of key temporal events in motor actions. Initially validated in MATLAB, the algorithmic framework was adapted into a cross-platform desktop application with a graphical user interface. Kinematic 4 processes coordinate data from markers placed on the thumb, index finger, wrist, object, and specialized glasses with a movable shutter. The program identifies six critical time points: experiment onset, hand lifting, finger opening, maximum grasp aperture, object lifting, and object placement.

Results. Comparison between results obtained using Kinematic 4 and those generated by the original MATLAB script demonstrated full consistency. The software was successfully validated on experimental datasets and showed high stability. Its user-friendly interface and automated workflow make it a reliable and reproducible tool for both research and clinical applications.

Conclusion. Kinematic 4 can be effectively used for assessing upper-limb movements in neuroscience and clinical contexts, including the diagnosis of motor impairments and monitoring of recovery dynamics. Future development may include integration with other biosignals and machine learning modules for predictive analytics.

About the authors

A. O. Vyazmin

Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, National Research University “Higher School of Economics”

Author for correspondence.
Email: aovyazmin@hse.ru
ORCID iD: 0000-0003-2346-4222

Aleksandr Olegovich Vyazmin,

20, Myasnitskaya St., Moscow 101000.

Russian Federation

M. R. Chapanova

School of Applied Mathematics, Tikhonov Moscow Institute of Electronics and Mathematics, National Research University “Higher School of Economics”

Email: fake@neicon.ru
ORCID iD: 0009-0003-4328-5325

34, Tallinskaya St., Moscow 123458.

Russian Federation

M. S. Morozov

School of Applied Mathematics, Tikhonov Moscow Institute of Electronics and Mathematics, National Research University “Higher School of Economics”

Email: fake@neicon.ru
ORCID iD: 0009-0003-9388-2849

34, Tallinskaya St., Moscow 123458.

Russian Federation

S. A. Aksenov

School of Applied Mathematics, Tikhonov Moscow Institute of Electronics and Mathematics, National Research University “Higher School of Economics”

Email: fake@neicon.ru
ORCID iD: 0000-0003-4403-7246

34, Tallinskaya St., Moscow 123458.

Russian Federation

M. Feurra

Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, National Research University “Higher School of Economics”

Email: fake@neicon.ru
ORCID iD: 0000-0003-0934-6764

20, Myasnitskaya St., Moscow 101000.

Russian Federation

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Copyright (c) 2025 Vyazmin A.O., Chapanova M.R., Morozov M.S., Aksenov S.A., Feurra M.

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