To solve the safety problems caused by the restriction of interaction space and the singular configuration of rehabilitation robot in terminal traction upper limb rehabilitation training, a trajectory planning and tracking control scheme for rehabilitation training is proposed. The human-robot safe interaction space was obtained based on kinematics modeling and rehabilitation theory, and the training trajectory was planned based on the occupational therapy in rehabilitation medicine. The singular configuration of the rehabilitation robot in the interaction space was avoided by exponential adaptive damped least square method. Then, a nonlinear controller for the upper limb rehabilitation robot was designed based on the backstepping control method. Radial basis function neural network was used to approximate the robot model information online to achieve model-free control. The stability of the controller was proved by Lyapunov stability theory. Experimental results demonstrate the effectiveness and superiority of the proposed singular avoidance control scheme.
In the diagnosis of pediatric pneumonia, the weak conductivity contrast of lung tissues leads to limited image resolution in electrical impedance tomography (EIT). To improve the quality of image reconstruction, this study proposes a radial basis function neural network optimized by the crested porcupine optimizer and adaptive moment estimation (CPOA-RBFNN). By integrating the global search capability of swarm intelligence with the adaptive characteristics of gradient based optimization, the proposed method enhances both imaging accuracy and robustness. A total of 23 000 simulated datasets of pediatric pneumonia were constructed for training and testing. The proposed method was compared with Tikhonov regularization and the conventional radial basis function neural network (RBFNN), and evaluated using root mean square error (RMSE), image correlation coefficient (ICC), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Results show that under a noise level of 50 dB, the proposed method achieves the lowest RMSE (0.098), the highest ICC (0.922), and relatively high SSIM (0.909) and PSNR (9.295). Further clinical validation demonstrates that CPOA-RBFNN provides superior structural fidelity and lesion distinguishability in pneumonia reconstruction. In conclusion, the proposed method offers an effective solution for non-invasive, high-precision imaging and bedside-assisted diagnosis of pediatric pneumonia.