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When a force is exerted on an object, it can cause that object to rotate about an axis. The moment of a force, also known as torque, measures the force's ability to cause that rotation. In the case of a cyclist pedaling a bicycle, the force exerted on the pedal causes the crankshaft to rotate, which in turn causes the wheel to spin. The moment of the force exerted on the pedal drives the wheel's rotation.
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The moment of a force about an axis is a crucial concept in mechanics that helps determine an object's rotational motion around a specific point or axis. The moment of force can be calculated using scalar analysis, which involves considering the perpendicular distance between the axis of rotation and the line of action of the force or simply the moment arm.
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A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling.

Reza Ahmadi1, Shahram Rasoulian2, Samira Fazeli Veisari3

  • 1Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

A new neural network (NN) model accurately predicts pedaling forces using readily available data. This low-cost machine learning approach enhances the study of cycling biomechanics for training and rehabilitation.

Keywords:
cyclingneural networkspedal reaction forceradial and mediolateral forces

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Area of Science:

  • Biomechanics
  • Sports Science
  • Machine Learning

Background:

  • Accurate pedaling kinetics measurement is crucial for optimizing athletic training, rehabilitation, and understanding musculoskeletal biomechanics.
  • Pedal reaction force is essential for musculoskeletal modeling but traditional sensor instrumentation is expensive and requires extensive postprocessing.
  • Machine learning (ML), specifically neural network (NN) models, offers a promising avenue for cost-effective kinetic analyses.

Purpose of the Study:

  • To develop and validate a neural network (NN) model for predicting radial and mediolateral pedal forces during cycling.
  • To provide a low-cost, accessible method for analyzing pedaling biomechanics using stationary cycling ergometers.
  • To assess the accuracy and computational efficiency of the NN model for real-time force predictions.

Main Methods:

  • Developed a neural network (NN) model utilizing data including pedal force, crank angle, cadence, power, and participant anthropometrics.
  • Collected pedal force data from fifteen healthy individuals performing pedaling tasks at self-selected and higher cadences using a 3-axis force system.
  • Evaluated the NN model's performance using inter-subject normalized root mean square error (nRMSE) for radial and mediolateral force predictions.

Main Results:

  • The NN model demonstrated strong predictive accuracy, achieving an inter-subject nRMSE of 0.15 ± 0.02 for radial and 0.26 ± 0.05 for mediolateral forces at high cadence.
  • At self-selected cadence, the model yielded nRMSE values of 0.20 ± 0.04 (radial) and 0.22 ± 0.04 (mediolateral).
  • The model exhibited low computational time, making it suitable for real-time pedal force estimations.

Conclusions:

  • The developed NN model provides an accurate and low-cost solution for estimating pedal forces in cycling biomechanics research.
  • This ML-driven approach can significantly aid in optimizing rehabilitation and exercise training by offering detailed kinetic insights.
  • The model's efficiency and accuracy align with advancements in ML for biomechanical data analysis, comparable to gait analysis algorithms.