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Related Experiment Video

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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic

Shashwati Geed1,2, Megan L Grainger2, Abigail Mitchell2

  • 1Department of Rehabilitation Medicine, Georgetown University, Washington, DC, United States.

Frontiers in Physiology
|April 10, 2023
PubMed
Summary

Machine learning accurately measures real-world upper extremity (UE) use in stroke survivors, correlating well with clinical assessments and video analysis. This technology offers a valid, feasible tool for tracking UE recovery in rehabilitation trials.

Keywords:
ADLsaccelerometrydisability evaluationmachine learningparesis/rehabilitationpsychometricssensorsstroke rehabilitation

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Area of Science:

  • Rehabilitation Medicine
  • Machine Learning in Healthcare
  • Neuroscience

Background:

  • Quantifying real-world upper extremity (UE) use is crucial for stroke recovery assessment.
  • Traditional methods like video analysis are labor-intensive, and self-report measures can be subjective.
  • Machine learning (ML) offers a potential for objective, scalable measurement of UE activity.

Purpose of the Study:

  • To validate ML-derived estimates of real-world UE use against video-based ground truth in stroke survivors.
  • To assess the correlation of ML-derived UE use with established measures of impairment, function, and dexterity.
  • To determine the feasibility of ML for measuring UE recovery in clinical trials.

Main Methods:

  • Participants (n=31) with chronic stroke wore accelerometers on both arms.
  • Video recording captured participants performing daily activities to establish ground-truth UE use.
  • A random forest classifier was trained on accelerometry data to estimate UE use ratio.

Main Results:

  • ML-estimated UE use ratio strongly correlated with video-derived ratios (Bland-Altman plots showed excellent agreement).
  • ML-derived use also correlated significantly with the Action Research Arm Test (ARAT), Fugl-Meyer (UEFM), and Nine-Hole Peg Test (9HPT).
  • ML-derived UE use explained 83% of the variance in UE motor performance, capturing similar constructs to clinical tests.

Conclusions:

  • The developed ML approach provides a valid and accurate measure of functional UE use in stroke survivors.
  • Its accuracy, validity, and minimal footprint make it suitable for large-scale UE recovery monitoring in rehabilitation.
  • This technology can enhance objective outcome measurement in stroke clinical trials.