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A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset.

Fei Wang1, Anqi Dong1, Kaiyu Zhang1

  • 1AI Sports Engineering Lab., School of Sports Engineering, Beijing Sport University, 48 Xinxi Road, Beijing 100084, China.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
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This study introduces a new RUS Boost classifier to objectively assess lower limb balance ability using inertial sensor data. The developed model significantly outperforms traditional methods, aiding in athlete selection and training.

Area of Science:

  • Biomechanics
  • Machine Learning
  • Sports Science

Background:

  • Balance ability is crucial for physical fitness, athletic performance, and daily activities.
  • Objective assessment of balance is challenging, particularly for non-professional athletes.
  • Inertial sensors offer a viable method for collecting lower limb movement data.

Purpose of the Study:

  • To develop and evaluate a novel machine learning classifier for objective balance ability assessment.
  • To differentiate between various levels of balance performance.
  • To provide a tool for initial screening and targeted training programs.

Main Methods:

  • Collected lower limb movement data using inertial sensors.
  • Extracted relevant feature parameters from the sensor data.
Keywords:
RUS Boostbalance performancelower limb datasetquantitative assessment modeltraining

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  • Designed a RUS Boost classifier with a Support Vector Machine (SVM) base for imbalanced datasets.
  • Compared the RUS Boost classifier's performance against K-Nearest Neighbors (KNN) and SVM classifiers.

    • Main Results:

    • The RUS Boost classifier achieved 72% accuracy in predicting balance degree.
    • Performance metrics showed the RUS Boost classifier outperformed KNN (60%) and SVM (44%).
    • The model demonstrated effectiveness in classifying different levels of lower limb balance.

    Conclusions:

    • The developed RUS Boost classification model is effective for quantitative assessment of lower limb balance ability.
    • This approach can be utilized for initial screening of balance capabilities.
    • The model supports the development of targeted training programs to improve balance.