This study proposes an explainable computational intelligence framework for badminton stroke-quality assessment with large language model (LLM)-assisted coaching analytics. The proposed framework integrates stroke event segmentation, skeletal motion analysis, graph-based action recognition, stroke-quality assessment, and natural-language feedback generation. Racket–shuttlecock contact moments are first detected to extract individual stroke clips. MediaPipe Pose is employed to obtain skeletal joint trajectories, from which six stroke performance indices are formulated, including swing velocity index, wrist trajectory smoothness index, wrist posture index, elbow posture index, footwork efficiency index, and racket control index. These indices are integrated to generate interpretable stroke-quality ratings and radar-chart visualizations. For stroke classification, the proposed mST-GCN++ model achieved 94.34% accuracy across 11 badminton stroke categories, while the racket–shuttlecock contact detection module achieved an F1-score of 99.74%. The recognized stroke type, stroke performance indices, and assessment results are subsequently provided to an LLM-based module to generate personalized coaching feedback. Experimental results demonstrate that the proposed framework can effectively distinguish effective and ineffective stroke executions through interpretable quantitative assessments. Human-in-the-loop evaluation with badminton experts and players achieved an average score of 8.594/10, indicating that the generated feedback is technically accurate, consistent with observed movements, and useful for practical training. The proposed framework contributes by introducing six interpretable stroke performance indices that bridge low-level skeletal motion and high-level coaching knowledge, a hierarchical fuzzy inference system that performs transparent biomechanical reasoning for stroke-quality assessment, and a knowledge-guided LLM-assisted coaching framework that generates grounded natural-language coaching feedback from structured assessment results.
Citation: Hsiang-Chieh Chen, Hsin-Lun Liu, Jun-Lung Hsiao, Pei-Yuan Hung. An explainable AI framework for badminton stroke quality assessment via hierarchical fuzzy inference and LLM-assisted coaching analytics[J]. Electronic Research Archive, 2026, 34(10): 7739-7778. doi: 10.3934/era.2026333
This study proposes an explainable computational intelligence framework for badminton stroke-quality assessment with large language model (LLM)-assisted coaching analytics. The proposed framework integrates stroke event segmentation, skeletal motion analysis, graph-based action recognition, stroke-quality assessment, and natural-language feedback generation. Racket–shuttlecock contact moments are first detected to extract individual stroke clips. MediaPipe Pose is employed to obtain skeletal joint trajectories, from which six stroke performance indices are formulated, including swing velocity index, wrist trajectory smoothness index, wrist posture index, elbow posture index, footwork efficiency index, and racket control index. These indices are integrated to generate interpretable stroke-quality ratings and radar-chart visualizations. For stroke classification, the proposed mST-GCN++ model achieved 94.34% accuracy across 11 badminton stroke categories, while the racket–shuttlecock contact detection module achieved an F1-score of 99.74%. The recognized stroke type, stroke performance indices, and assessment results are subsequently provided to an LLM-based module to generate personalized coaching feedback. Experimental results demonstrate that the proposed framework can effectively distinguish effective and ineffective stroke executions through interpretable quantitative assessments. Human-in-the-loop evaluation with badminton experts and players achieved an average score of 8.594/10, indicating that the generated feedback is technically accurate, consistent with observed movements, and useful for practical training. The proposed framework contributes by introducing six interpretable stroke performance indices that bridge low-level skeletal motion and high-level coaching knowledge, a hierarchical fuzzy inference system that performs transparent biomechanical reasoning for stroke-quality assessment, and a knowledge-guided LLM-assisted coaching framework that generates grounded natural-language coaching feedback from structured assessment results.
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Hsiang-Chieh Chen, Hsin-Lun Liu, Jun-Lung Hsiao, Pei-Yuan Hung. An explainable AI framework for badminton stroke quality assessment via hierarchical fuzzy inference and LLM-assisted coaching analytics[J]. Electronic Research Archive, 2026, 34(10): 7739-7778. doi: 10.3934/era.2026333
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