With the continuous increase in the global aging population, stroke has become one of the major diseases affecting the health of the elderly, and the upper limb motor dysfunction it causes often requires long-term rehabilitation. To improve rehabilitation outcomes for hemiplegic patients and alleviate the shortage of rehabilitation physicians, upper limb rehabilitation robots have shown great potential in enhancing motor function and improving stroke patients’ rehabilitation outcomes in clinical research. This paper first classifies rehabilitation robots based on their driving mechanisms and interaction modes, describing the application of their structural features in various scenarios. It then analyzes the optimization methods used in the trajectory planning process of rehabilitation robots at different stages. Finally, based on existing shortcomings, the paper summarizes the future development directions of upper limb rehabilitation robots, providing prospects for the development of upper limb rehabilitation robots in the areas of artificial intelligence and compliant control, multi-sensory feedback and interactive training, ergonomics and new driving technologies, modular and customizable designs, and multi-modal brain stimulation techniques.

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation.