Integrated software for multi-dimensional analysis of motion using tracking, electrophysiology, and sensor signals

Area of Science:

• Biomechanics
• Kinesiology
• Neuroscience
• Robotics
• Computer Vision

Background:

• Motion tracking from video and motion capture systems are established methods for analyzing movement in diverse scientific fields.
• Integrating motion tracking with other data, such as muscle activity or sensor signals, provides deeper insights into movement dynamics.
• Manual analysis of multidimensional movement data is often time-consuming and susceptible to errors.

Purpose of the Study:

• To introduce a novel, free, and open-source MATLAB-based software tool designed for comprehensive multidimensional motion data analysis.
• To provide researchers with an accessible and versatile platform for processing and analyzing complex movement data.
• To streamline the analysis of kinematic and other physiological data, enhancing research efficiency and accuracy.

Main Methods:

• Development of a MATLAB-based software with a user-friendly graphical interface.
• Implementation of tools for importing and preprocessing 2D/3D coordinate data from single or multiple cameras.
• Integration of modules for analyzing gait cycles, calculating kinematic parameters, and generating animations.
• Inclusion of functionalities for importing, filtering, and correlating electrophysiology traces and sensor signals with kinematic data.

Main Results:

• The software enables automated calculation of kinematic parameters and descriptive statistics from imported motion data.
• It facilitates the generation of 2D and 3D animations for visualizing movement.
• The tool successfully integrates and analyzes kinematic data with electrophysiology and sensor signals.
• User-friendly interface allows complex movement analysis without requiring coding expertise.

Conclusions:

• The developed software offers a powerful, versatile, and accessible solution for analyzing complex multidimensional movement data.
• Its ability to integrate diverse data sources and automate analysis significantly reduces time and potential errors compared to manual methods.
• This open-source tool is valuable for researchers across various scientific disciplines, supporting a wide range of experimental applications.