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Identification of footstrike pattern using accelerometry and machine learning.

Joseph M Mahoney1, Matthew B Rhudy2, Jereme Outerleys3

  • 1Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA, USA; Kinesiology, The Pennsylvania State University, Berks College, Reading, PA, USA; Mechanical Engineering, Alvernia University, Reading, PA, USA.

Journal of Biomechanics
|August 19, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately detects running footstrike patterns using tibial acceleration data. This technology, utilizing artificial neural networks, can be integrated into wearable sensors for real-time analysis and injury prevention.

Keywords:
AccelerometerArtificial neural networkFootstrike patternGait identificationWearable sensor

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Area of Science:

  • Biomechanics of running
  • Sports injury prevention
  • Machine learning applications in sports

Background:

  • Growing evidence links running footstrike patterns to overuse injuries.
  • Wearable sensors offer potential for real-time biomechanical analysis.
  • Need for efficient methods to detect footstrike patterns from minimal sensor data.

Purpose of the Study:

  • To develop and evaluate a machine learning model for real-time footstrike pattern classification.
  • To determine the accuracy of classifying rearfoot, midfoot, and forefoot strikes using tibial accelerometry.
  • To assess the feasibility of integrating this technology into wearable devices.

Main Methods:

  • Collected tibial accelerometry data from 58 participants running with three distinct footstrike patterns (rearfoot, midfoot, forefoot).
  • Utilized an artificial neural network classifier to analyze acceleration data from varying percentages (100%, 75%, 40%) of the stance phase.
  • Employed a data-driven approach without manual feature selection or data filtering.

Main Results:

  • The machine learning models achieved up to 89.9% average accuracy in classifying footstrike patterns.
  • The highest classification error was observed between midfoot and forefoot strike patterns.
  • The method demonstrated effectiveness using reduced data sets (75% and 40% of stance phase).

Conclusions:

  • Machine learning, specifically artificial neural networks, can accurately detect running footstrike patterns from tibial acceleration data.
  • This approach is suitable for real-time application in wearable devices for injury risk assessment.
  • Further refinement may be needed to improve discrimination between midfoot and forefoot strikes.