  The utility of machine learning in understanding the motor system is
promising a revolution in how to collect, measure, and analyze data. The field
of movement science already elegantly incorporates theory and engineering
principles to guide experimental work, and in this review we discuss the
growing use of machine learning: from pose estimation, kinematic analyses,
dimensionality reduction, and closed-loop feedback, to its use in understanding
neural correlates and untangling sensorimotor systems. We also give our
perspective on new avenues where markerless motion capture combined with
biomechanical modeling and neural networks could be a new platform for
hypothesis-driven research.
