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    This study developed an automatic cycling performance measurement system using a Fuzzy Logic Controller (FLC) to classify cyclist performance. The system accurately assesses cyclist ability from power output and force asymmetry data.

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    Area of Science:

    • Sports Science
    • Biomechanical Engineering
    • Control Systems

    Background:

    • Objective measurement of cyclist performance is crucial for training and analysis.
    • Existing methods may lack precision or automation in performance assessment.
    • Force asymmetry and power output are key indicators of cycling efficiency.

    Purpose of the Study:

    • To develop and validate an automated system for cyclist performance measurement.
    • To utilize Fuzzy Logic Controller (FLC) for classifying cyclist performance.
    • To integrate data from power output and force asymmetry for a comprehensive performance score.

    Main Methods:

    • Development of an automatic cycling performance measurement system incorporating a Fuzzy Logic Controller (FLC) with Mamdani Inference.
    • Acquisition of cycling data using a custom-built crank arm load cell force platform with high linearity (<0.6%).
    • Experimental design involving 15 cyclists, measuring average power, power standard deviation, and effective force bilateral asymmetry index.

    Main Results:

    • The developed FLC system successfully determined cyclist performance scores, with a mean score of 25.4% ± 16.9%.
    • Average power output was 137.63±59.6W, and the mean bilateral asymmetry index was 67.01±6.23%.
    • Analysis of Variance (ANOVA) confirmed significant subject-induced variation in performance scores.

    Conclusions:

    • The developed FLC-based system provides an effective and automated method for classifying cyclist performance.
    • The system's reliance on power and force asymmetry data offers a robust approach to performance evaluation.
    • Individual physiological differences significantly impact cycling performance outcomes.