A framework for closing the loop between human experts and computational algorithms for the assessment of movement disorders.

Clinical assessment of abnormal neuromechanics is typically performed by manipulation of the affected limbs; a process with low inter- and intra-rater reliability. This paper aims at formalizing a framework that closes the loop between a clinician's expertise and computational algorithms, to enhance the clinician's diagnostic capabilities during physical manipulation. The framework's premise is that the dynamics that can be measured by manipulation of a limb are distinct between movement disorders. An a priori database contains measurements encoded in a space called the information map. Based on this map, a computational algorithm identifies which probing motions are more likely to yield distinguishing information about a patient's movement disorder. The clinician executes this movement and the resulting dynamics, combined with clinician input, is used by the algorithm to estimate which of the movement disorders in the database are most probable. This is recursively repeated until a diagnosis can be confidently made. The main contributions of this paper are the formalization of the framework and the addition of the information map to select informative movements. The establishment of the framework provides a foundation for a standardized assessment of movement disorders and future work will aim at testing the framework's efficacy.