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Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
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Computer Vision for Movement Observation and Recovery Enhancement (C-MORE): Box and Blocks Test.

Jun Min Kim1, Ziqiang Joe Zhu1, Hari Venugopalan1

  • 1Computer Science Department, University of California Davis, Davis, CA 95616, USA.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
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This summary is machine-generated.

Smartphone computer vision accurately assesses upper-extremity function after stroke. C-MORE (Computer Vision for Movement Observation and Recovery Enhancement) provides scalable, precise measurements for better rehabilitation.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Computer Vision

Background:

  • Stroke causes diverse sensorimotor deficits, often poorly measured by standard clinical tools.
  • Advanced motion capture and robotics offer precision but lack clinical scalability.
  • There is a need for accessible, quantitative assessments of upper-extremity function post-stroke.

Purpose of the Study:

  • To develop and evaluate C-MORE (Computer Vision for Movement Observation and Recovery Enhancement), a smartphone-based system for automated scoring of the Box and Blocks Test (BBT).
  • To extract quantitative kinematic metrics for assessing sensorimotor impairments in individuals with stroke.
  • To provide a scalable and practical solution for enhancing clinical evaluation and rehabilitation.

Main Methods:

Keywords:
clinical assessmentcomputer visionrehabilitationstrokeupper-extremity kinematics

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  • Developed C-MORE, a framework using smartphone computer vision and machine learning for hand tracking and task segmentation.
  • Implemented a custom ML architecture to automatically score valid block transfers in the BBT.
  • Evaluated C-MORE in 7 individuals with chronic stroke and 10 healthy adults, comparing system output to ground-truth scoring.

    • Main Results:

    • C-MORE achieved 99.0% agreement with ground-truth scoring, with errors below clinically significant thresholds.
    • Extracted kinematic measures demonstrated sensitivity to stroke-related impairments, such as reduced movement velocity and increased task duration.
    • Exploratory analysis revealed correlations between grasp-related metrics and proprioception in affected limbs.

    Conclusions:

    • Smartphone-based computer vision offers an accurate and scalable method for assessing upper-extremity function.
    • C-MORE provides a practical tool for improving clinical evaluation and enabling personalized stroke rehabilitation strategies.
    • The system's ability to capture detailed kinematic data can inform more precise therapeutic interventions.