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

Objective

To predict the punching force in Wushu Sanda following neuromuscular electrical stimulation (NMES) using the KAN neural network combined with neuromuscular electricity.

Methods

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.

Results

NMES significantly enhanced punching force metrics (all p < 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.

Conclusions

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.