Predicting the Punching Force in Wushu Sanda After Neuromuscular Electrical Stimulation by Employing the KAN Neural Network Combined with Neuromuscular Electricity

<b>Objective:</b> To predict the punching force in Wushu Sanda following neuromuscular electrical stimulation (NMES) using the KAN neural network combined with neuromuscular electricity. <b>Methods:</b> Thirty healthy Wushu Sanda athletes underwent a randomized repeated-measures design with three conditions: upper-limb NMES, lower-limb NMES, and Sham stimulation. Surface electromyography (sEMG) signals and punching force parameters were collected. A KAN neural network model was designed to integrate sEMG features and predict punching force. Model performance was evaluated using RMSE, MAE, and R² metrics. <b>Results:</b> NMES significantly enhanced punching force metrics (all <i>p</i> < 0.05). lower-limb NMES showed superior improvements in relative peak force (28.2 ± 3.2 N·kg^-1), impulse (16.6 ± 2.3 N s), and early explosive force (754 ± 94 N) compared to Sham and upper-limb NMES. The KAN model demonstrated robust predictive performance, particularly under lower-limb NMES conditions, with R² values of 0.59 for relative peak force and 0.58 for impulse. <b>Conclusions:</b> NMES, especially lower-limb stimulation, effectively boosts punching force. The KAN neural network provides a promising and innovative approach for predicting punching force in Wushu Sanda, providing a foundation for future optimization of real-time monitoring tools.