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Related Experiment Video

Updated: Jul 2, 2025

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

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A new method applied for explaining the landing patterns: Interpretability analysis of machine learning.

Datao Xu1,2,3, Huiyu Zhou1,2, Wenjing Quan1,2

  • 1Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China.

Heliyon
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

Explainable machine learning (XML) using Layer-wise Relevance Propagation (LRP) accurately interprets landing patterns for clinical biomechanics. This approach enhances transparency in diagnosing movement patterns, aiding clinical experts.

Keywords:
BiomechanicsClinical diagnosisExplainable machine learningLanding pattern recognitionLayer-wise relevance propagation

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Area of Science:

  • Biomechanics
  • Clinical Biomechanics
  • Sports Science

Background:

  • Landing maneuvers are crucial in sports and clinical injury screening.
  • Variations in landing patterns under different constraints complicate clinical diagnosis.
  • Traditional machine learning (ML) models lack transparency, acting as 'black boxes' in clinical decision-making.

Purpose of the Study:

  • To validate the feasibility of an explainable machine learning (XML) model for landing pattern recognition.
  • To utilize Layer-wise Relevance Propagation (LRP) for interpreting ML-driven landing pattern analysis.
  • To provide a transparent and interpretable framework for ML in clinical biomechanics.

Main Methods:

  • Collected and analyzed 560 groups of landing data.
  • Developed an XML model incorporating Layer-wise Relevance Propagation (LRP).
  • Interpreted prediction results using relevance scores (RS) from LRP, validated with Statistical Parametric Mapping (SPM) and Effect Size.

Main Results:

  • The XML model demonstrated feasibility in recognizing landing patterns.
  • Relevance scores (RS) from LRP showed excellent statistical characteristics for inter-class landing pattern interpretation.
  • The RS findings align with clinical characteristics of landing pattern recognition.

Conclusions:

  • Explainable ML (XML) methods, specifically LRP, offer a transparent solution for landing pattern recognition.
  • This approach addresses the 'black box' limitation of traditional ML in clinical biomechanics.
  • A feasible framework for interpretable ML in landing analysis is provided, supporting future clinical diagnosis and research.