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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
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Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

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

Vaidehi Wagh1, Matthew W Scott1,2, Justin W Andrushko3

  • 1Neuroplasticity, Imagery, and Motor Behaviour Laboratory, Department of Psychology, University of British Columbia, BC, V1V 1V7, Kelowna, Canada.

Journal of Neuroengineering and Rehabilitation
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence using MediaPipe Pose Landmarker can track upper limb movements for stroke recovery assessment. This technology quantifies hand, shoulder, and trunk kinematics, aiding in motor recovery evaluation.

Keywords:
Artificial intelligenceKinematicsMarkerless pose estimationMotion trackingMotor impairment

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Area of Science:

  • Rehabilitation Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Markerless motion capture systems show promise for assessing post-stroke motor recovery.
  • MediaPipe Pose Landmarker is an accessible AI tool for tracking upper limb movements using a single camera.

Purpose of the Study:

  • To evaluate MediaPipe Pose Landmarker for tracking upper limb movements in individuals after stroke.
  • To quantify kinematic outcomes related to hand, shoulder, and trunk movements.

Main Methods:

  • Seven participants with chronic stroke performed a gamified reaching task over five sessions.
  • Video recordings were processed using MediaPipe Pose Landmarker to extract joint coordinates.
  • Kinematic variables including mean palm speed and bivariate variable error (BVE) were analyzed.

Main Results:

  • Exploratory analyses revealed increased mean palm speed and palm BVE over time.
  • Shoulder and trunk movement patterns potentially correlated with hand movement improvements in some participants.
  • The system successfully tracked upper limb movements in a 2D Cartesian space.

Conclusions:

  • MediaPipe Pose Landmarker is a viable tool for tracking upper limb movements in stroke survivors.
  • This AI-based system can complement traditional clinical assessments of motor recovery.
  • Findings support the use of markerless motion capture for objective motor function evaluation.