A spatio-temporal graph convolutional meta-learning framework for high-precision biomechanical analysis of badminton strokes

Seventy-three percent of badminton athletes face technical bottlenecks. Traditional analysis relies on empirical observation and lacks a quantitative analysis of deep biomechanical parameters, such as joint torque and tendon tension. This study proposes a general framework based on dual-channel spatio-temporal graph representation and meta-learning optimization for high-fidelity reconstruction, estimation, and few-shot recognition of multimodal biomechanical temporal data. The framework constructs a dynamic graph structure by integrating physical and functional connectivity, and achieves rapid adaptation through a hierarchical parameter update strategy. Four-dimensional time series tensors, including joint angle, tendon tension, and other dimensions, were constructed by integrating optical capture, inertial measurement, and surface electromyography data through millimeter-level synchronization technology. The physical functional dual-channel biomechanical map structure was then established based on anatomical constraints. Experimental results show that the framework achieves 93.7% recognition accuracy in 12,340 strokes, which is 12.3% and 5.5% higher than the traditional 3D-CNN (81.4%) and the state-of-the-art Model-Agnostic Meta-Learning (MAML, 88.2%) methods, respectively. The tendon tension reconstruction error is reduced to 6.8 N, representing a 63% improvement over the pure mechanical model (18.6 N). With only five athlete samples, a 92% generalization rate is achieved, significantly outperforming typical few-shot learning models like Prototypical Networks (68.3%). Furthermore, the end-to-end processing delay is compressed to 98.2 ms, enabling near-real-time informational feedback suitable for post-hoc analysis and coaching support, meeting the latency requirements for potential industrial-grade applications. These quantitative comparisons demonstrate the high-precision advantage of the proposed framework for diagnostic assessment and feedback systems, rather than direct modeling of skill acquisition or perceptual control mechanisms. While the methodological components have been validated on badminton stroke analysis, the dual-channel graph representation and hierarchical meta-learning strategy have been designed with potential transferability to other human motion analysis tasks in mind. However, cross-domain validation remains a direction for future work.