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Related Experiment Video

Updated: May 21, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
07:51

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Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos.

Cancan Su1, Lianne Brandt1, Guangwen Sun1

  • 1Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON K1H 8L1, Canada.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered system using smartphone videos to automate Modified Mallet Score (MMS) assessments for upper limb function, achieving high accuracy and enabling remote evaluations.

Keywords:
OpenPoseclinically relevant interpretationmodified Mallet scorepose estimationsmartphonetwo-dimensional coordinatesvideo

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Area of Science:

  • Medical technology
  • Artificial intelligence
  • Biomechanics

Background:

  • The Modified Mallet Score (MMS) is a standard clinical tool for assessing upper limb function.
  • Current MMS evaluation requires experienced clinicians, limiting accessibility and scalability.
  • There is a need for objective, remote, and efficient methods for MMS assessment.

Purpose of the Study:

  • To develop and validate an automated system for Modified Mallet Score (MMS) assessment using artificial intelligence (AI) and smartphone videos.
  • To evaluate the accuracy and reliability of the AI-driven MMS scoring system compared to expert clinical assessments.
  • To explore the potential of AI-based remote assessment for upper limb function.

Main Methods:

  • Utilized smartphone videos from four participants covering all MMS grades.
  • Employed the OpenPose BODY25 model to extract body keypoint data from videos.
  • Developed an algorithm to calculate joint angles and automate MMS scoring.
  • Compared automated scores against expert physician manual scoring (ground truth).

Main Results:

  • The automated system demonstrated high accuracy for key upper limb movements, including global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth.
  • Achieved high reliability with Pearson correlation coefficients (PCCs) greater than 0.9 and low root mean square error (RMSE).
  • Showed strong agreement for global external rotation, despite slightly lower accuracy compared to other movements.

Conclusions:

  • AI-powered analysis of smartphone videos offers a reliable method for automating MMS assessments.
  • This technology has the potential to facilitate remote, objective, and accessible upper limb functional evaluations.
  • The findings support the integration of AI in clinical practice for efficient patient monitoring and assessment.