Scholarlink

Purpose <p>To design a coach-interpretable movement quality score for badminton footwork training using an adaptive neuro-fuzzy inference system.</p> Methodology <p>We derived compact biomechanical descriptors of timing, kinematics, dynamics, and stability from benchmark motion recordings and trained a first order Takagi Sugeno ANFIS with Gaussian membership functions and hybrid learning using a 70 15 15 train validation test split with early stopping.</p> Results <p>The model achieved RMSE 0.074, MAE 0.058, and R2 0.91 on the test set and outperformed linear regression, multilayer perceptron, and support vector regression while preserving transparent fuzzy rules.</p> Conclusion <p>ANFIS provides accurate and explainable quality estimation.</p> Recommendations <p>Future work should validate on in-court badminton datasets, expand features for foot contact and center of mass, and deploy lightweight wearable inference for practical real-time feedback.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Applying neuro-fuzzy modeling to evaluate and enhance badminton footwork training

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

查看全文
  • Jiayun Xu

摘要

Purpose

To design a coach-interpretable movement quality score for badminton footwork training using an adaptive neuro-fuzzy inference system.

Methodology

We derived compact biomechanical descriptors of timing, kinematics, dynamics, and stability from benchmark motion recordings and trained a first order Takagi Sugeno ANFIS with Gaussian membership functions and hybrid learning using a 70 15 15 train validation test split with early stopping.

Results

The model achieved RMSE 0.074, MAE 0.058, and R2 0.91 on the test set and outperformed linear regression, multilayer perceptron, and support vector regression while preserving transparent fuzzy rules.

Conclusion

ANFIS provides accurate and explainable quality estimation.

Recommendations

Future work should validate on in-court badminton datasets, expand features for foot contact and center of mass, and deploy lightweight wearable inference for practical real-time feedback.