Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use.

ABSTRACT: To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of "normal movement" and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared

SUBMITTER: Wang SL 

PROVIDER: S-EPMC7877744 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature