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Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning

Chien-Pin Liu1, Ting-Yang Lu2, Hsuan-Chih Wang1

  • 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei City 112, Taiwan.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
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This summary is machine-generated.

This study introduces an inertial measurement unit (IMU)-based system for objective frozen shoulder (FS) assessment. Machine learning accurately identifies shoulder tasks in FS patients, offering a feasible clinical tool.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Frozen shoulder (FS) causes shoulder pain and limited range of motion (ROM), with current assessments being subjective.
  • Objective evaluation methods are needed to improve the accuracy of FS diagnosis and monitoring.
  • Inertial Measurement Units (IMUs) offer a potential solution for objective motion analysis.

Purpose of the Study:

  • To develop and evaluate an IMU-based system for automatically identifying shoulder tasks in individuals with and without FS.
  • To compare the effectiveness of different machine learning (ML) and deep learning (DL) models for FS identification.
  • To analyze the impact of feature types and sensor placement on identification performance.

Main Methods:

Keywords:
frozen shoulderidentification systeminertial measurement unitmachine learning

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  • Utilized two IMUs attached to the arm and wrist of 24 FS patients and 20 healthy subjects performing five daily shoulder tasks.
  • Extracted and analyzed two feature groups: time-domain statistical features and kinematic features.
  • Applied seven ML techniques and two DL models (Convolutional Neural Network, Multilayer Perceptron) for task identification.

    • Main Results:

    • The DL-based system achieved the highest identification performance, with the CNN reaching 88.26% accuracy and MLP achieving an 89.23% F1 score.
    • Wrist-based IMU features yielded higher accuracy than arm-based features.
    • Time-domain statistical features demonstrated better discriminability for FS identification compared to kinematic features.

    Conclusions:

    • An IMU-based system using ML is feasible for objective FS assessment in clinical practice.
    • Deep learning models, particularly CNNs, show significant potential for accurate FS task identification.
    • Optimizing sensor placement (wrist) and feature selection (time-domain) can enhance system performance.