Purpose <p>Estimation of hand kinematics from surface electromyography (sEMG) signals is crucial for the effective control of multi-degree-of-freedom (multi-DoF) robotic neural interfaces. However, the neural coding model has not been fully clarified, which restricts the ability to extract motor information from neural signals. Due to insufficient information mining, current methods to estimating biological kinematics have inherent limitations in accuracy, stability, and generalization ability.</p> Methods <p>A scalogram image-based method for continuous estimation on multi-DoF finger joint angles was presented. Specifically, the continuous wavelet transform (CWT) was applied to sEMG signals to obtain scalogram images, and then to reduce the dimensionality of these images via digital image processing (DIP). Subsequently, the processed images were input into a convolutional neural network (CNN) to enable end-to-end autonomous learning. We selected all 40 subjects from the Ninapro DB2 dataset to verify the performance of the proposed method. In addition, we set up two independent control experiments, i.e., CNN method with sEMG image (sEMGimage-CNN) and support vector regression method with CWT (CWT-SVR).</p> Results <p>The experimental results demonstrated that the proposed method achieved an average correlation coefficient (CC) of 0.9793 ± 0.0089 and an average normalized root mean square error (nRMSE) of 0.0490 ± 0.0073, outperforming the other two methods in estimation accuracy.</p> Conclusion <p>These results demonstrate that the proposed method provides an effective approach for continuous estimation of hand kinematics. This work opens up a new perspective for myoelectric control and potentially achieve more natural human–computer interaction (HCI) in practical application.</p>