Measuring and modeling the motor system with machine learning.

Area of Science:

• Movement science
• Biomechanical engineering
• Neuroscience

Background:

• The field of movement science integrates theory and engineering.
• Machine learning (ML) offers advanced tools for data collection, measurement, and analysis.
• Understanding the motor system is crucial for various scientific and medical applications.

Purpose of the Study:

• To review the expanding applications of machine learning in movement science.
• To discuss ML's role in analyzing motor system data and neural correlates.
• To propose future research avenues integrating ML with biomechanical and neural network approaches.

Main Methods:

• Literature review of machine learning applications in movement science.
• Discussion of ML techniques including pose estimation, kinematic analysis, and dimensionality reduction.
• Exploration of closed-loop feedback systems and neural network applications.

Main Results:

• Machine learning significantly enhances the analysis of complex motor system data.
• Markerless motion capture combined with ML and biomechanical modeling presents a novel research platform.
• ML aids in understanding neural correlates and sensorimotor integration.

Conclusions:

• Machine learning is poised to revolutionize motor system research.
• Future research can leverage ML for hypothesis-driven investigations in movement science.
• Integrating advanced ML techniques offers new insights into neural and sensorimotor functions.