The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.

ABSTRACT: Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impairment in individuals using kinematic data, however, can be challenging. Machine learning techniques offer a potential solution to this problem. In the present manuscript we examine proprioception in stroke survivors using a robotic arm position matching task. Proprioception is impaired in 50-60% of stroke survivors and has been associated with poorer motor recovery and longer lengths of hospital stay. We present a simple cut-off score technique for individual kinematic parameters and an overall task score to determine impairment. We then compare the ability of different machine learn

SUBMITTER: Hossain D 

PROVIDER: S-EPMC9881388 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature