Using MediaPipe to track upper-limb reaching movements after stroke: a proof-of-principle study.

ABSTRACT: Emerging work supports the use of artificial intelligence-based markerless motion capture systems to complement standardized clinical measures when assessing post-stroke motor recovery. MediaPipe Pose Landmarker is an open-sourced, machine learning tool, requiring only one camera, which can be used to track upper limb movements and quantify kinematics. Here we aimed to test the use-case of MediaPipe Pose Landmarker in tracking upper limb movements after stroke in a 2-dimensional cartesian coordinate space. Participants (N = 7, > 2 months after stroke, upper extremity portion of the Fugl-Meyer Assessment (FMA-UE) range of 40-66) engaged in five sessions of a previously established, semi-immersive, gamified reaching task, involving movements of the hand and arm. Movements at four time points

SUBMITTER: Wagh V 

PROVIDER: S-EPMC12752325 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature