Distribution Analysis for Diagnostics and Therapeutics of Motor Actions

Treatment planning and monitoring for motor neurorehabilitation still relies mostly on coarse clinical scales and clinician observation. Distribution analysis of action patterns recorded by sensors can provide a quantitative and easily visualizable representation of complex individualized dynamics. Here, we review the use of distributions to analyze human action in health and following neurologic injury. The aim is to demonstrate the potential of distribution analysis as a simple yet effective framework for scanning a patient prior to therapy, assisting clinicians in quantitative evaluations, and thereby uncovering underlying pathophysiological mechanisms to optimize therapy. In this review paper, we first describe how idealized distributions can model movement and capture spatiotemporal patterns. We then review metrics that can holistically quantify changes between distributions. Next, we highlight applications in different data domains that have used these approaches and precise comparison techniques to identify motor deficits and personalize therapy. Together, these methods have uncovered upper extremity flexion synergy patterns in half of stroke survivors, improved classification accuracy in data-driven models, and reduced movement errors threefold. Finally, we discuss the challenges and opportunities facing the application of distribution analysis for neurorehabilitation and precision medicine.