A Matlab toolbox for analyzing repetitive movements: application in gait and tapping experiments.

Area of Science:

• Motor control and neuroscience research.
• Clinical biomechanics and neurological disorder assessment.

Background:

• Repetitive movements are crucial for understanding motor coordination in healthy individuals and neurological conditions.
• Existing tools for analyzing movement data in clinical settings, particularly for Parkinson's disease, are limited.
• Theoretical models like the Wing-Kristofferson (WK) and Haken-Kelso-Bunz (HKB) models provide frameworks for studying motor coordination.

Purpose of the Study:

• To introduce a novel, open-source toolbox for the quantitative analysis of repetitive movements.
• To enable analysis within the established WK and HKB models of motor coordination.
• To support diverse experimental settings, including self-paced vs. cued and uni-manual vs. bi-manual tasks.

Main Methods:

• Development of a toolbox with specialized digital signal processing pipelines.
• Application of the toolbox to analyze gait and tapping movements.
• Demonstration on data from a healthy control subject and a Parkinson's disease patient, including deep brain stimulation (DBS) ON/OFF conditions.

Main Results:

• The toolbox facilitates the analysis of repetitive movements in various experimental contexts.
• Illustrative examples show the toolbox's utility in differentiating movement patterns between healthy and Parkinson's disease subjects.
• The analysis includes data from a patient with deep brain stimulation, comparing effects of stimulation ON and OFF.

Conclusions:

• The developed toolbox offers a versatile and accessible solution for analyzing repetitive movements.
• It bridges the gap between basic research models (WK, HKB) and clinical applications, especially for Parkinson's disease.
• The open-source nature and freely accessible data promote further research and clinical trial advancements.