Explainable Badminton Coaching via Knowledge-guided Multimodal Anomaly Detection

Badminton strokes require highly coordinated movements across body parts, spanning foot loading, trunk rotation, and racket-arm muscle control. This makes coaching difficult with the naked eye and videos alone, as most of these signals are invisible. Recent badminton AI coaches have begun to incorporate wearable sensors. However, without a biomechanically organized modeling process, the errors they flag cannot be verifiably tied to the body part and stroke phase where they occur. Prior works also either stop at stroke- and skill-level classification, or issue coaching feedback that is neither explainable nor traceable to evidence. To address these limitations, we propose $\text{Badminton Intelligent Coach}$ ($\text{BIC}$), a multimodal framework for explainable badminton-stroke coaching from wearable sensors. We treat coaching as anomaly detection, with a conditional latent diffusion model operating over structured representations of skeletal motion, muscle activity, plantar pressure, and eye gaze, organized by a body-part-sensor graph that links biomechanically related signals. Detected deviations are then verified and transformed into coaching errors via a two-level knowledge graph and a reasoning multimodal large language model. Experiments on backhand drive and forehand clear strokes from the MultiSenseBadminton dataset show that BIC outperforms existing methods in both skill-level discrimination and anomaly detection. In blind evaluations, human badminton experts also rate BIC as more comprehensive and professional.