Fig. 1. FBG sensor configuration. (a) Grating arrangement; (b) sensor cross-section

Fig. 2. Sensor spectral shift error. (a) FBG spectral cumulative variation in multiple measurements; (b) spectral drift of two periodic experiments

Fig. 3. Comprehensive technical framework for FBG-based shape reconstruction. (a) Experimental setup and data acquisition; (b) signal preprocessing; (c) neural network architecture

Fig. 4. MSEs of STFT, WT, and EMD feature extraction methods. (a) MSE comparison; (b) MSE variations

Fig. 5. Schematic diagram of hyperparameter optimization by PSO and comparison with GA and BO methods. (a)‒(d) Hyperparameter optimization by PSO; (e) feature importance analysis; (f) MSE comparison with GA and BO

Fig. 6. Overall methodology framework

Fig. 7. Training-validation loss convergence profiles

Fig. 8. MSE distributions among model configurations in ablation study

Fig. 9. Curvature prediction results analysis. (a) Box plot of comparison for 4D actual curvature and predicted curvature; (b) residual kernel density of predicted curvature

Fig. 10. Comparison of true values and reconstructed values under different models. (a) Constant-curvature circular arc curve; (b) variable-curvature circular arc curve; (c) variable-curvature S-shaped curve; (d) reverse S-shaped curve