In recent years, the increasing number of patients with spinal cord injuries, strokes, and lower limb disabilities has led to the gradual development of rehabilitation-assisted exoskeleton robots. A critical aspect of these robots is their ability to accurately sense human movement intentions to achieve smooth and natural control. This paper describes research carried out on predicting the motion angles of human lower limb joints. Based on the design of a signal acquisition system for physiological muscle signals and inertial measurement unit (IMU) data, a hybrid neural network prediction model (QRTCN-BiLSTM) and a single neural network prediction model (QRBiLSTM) were constructed using quantile regression, temporal convolution network (TCN) and bidirectional long short-term memory network (BiLSTM), respectively. At the same time, 7-channel surface electromyographic signals (sEMG) and 12-channel IMU data from hip and knee joints were collected and input into the QRBiLSTM and QRTCN-BiLSTM models to unfold the training and analyze the comparison. The results show that the QRTCN-BiLSTM model can more accurately infer human movement intention and provide a more reliable and accurate prediction tool for human–robot interaction research in rehabilitation robotics.

Rigid–flexible coupled robots have problems such as vibration and elastic deformation caused by the flexibility of the members during the motion process, which significantly impacts the system’s motion accuracy and dynamics performance. To address the above problems, a dynamic modeling method based on a vector bond graph is proposed, and a multi-energy domain global dynamic model of Delta-type rigid–flexible coupled parallel robot considering rod flexibility is established. The coupled vibration of the control part and mechanical part of the system is analyzed, and model simulation is verified by 20-sim 4.4 software and ADAMS software (instructional version), which verifies the validity and reasonableness of the modeling method and provides a reference for the modeling of other rigid–flexible coupled systems with parallel systems in space.

To improve the accuracy of fatigue assessment in patients undergoing upper limb rehabilitation, this study proposes a lightweight fatigue detection algorithm based on ensemble learning and single-channel surface electromyography (sEMG) signals. Thirty healthy subjects without upper limb injuries or severe chronic diseases were recruited, and dynamic sEMG signals of the biceps brachii were collected during dumbbell bicep curls at a sampling frequency of 2048 Hz, yielding 6650 valid experimental samples. To address the class imbalance in the sEMG dataset, the SMOTETomek hybrid sampling algorithm was employed for data balancing. Three ensemble strategies—voting, stacking, and mean fusion—were integrated and combined with four different sets of base classifiers to construct a total of 12 fusion models, and the optimal model was identified through comparative screening. The experimental results demonstrated that, after SMOTETomek sample balancing, the LightGBM-LR-MLP soft voting ensemble model achieved the best overall performance, with the accuracy, recall, precision, and F1-score for both fatigue categories all exceeding 0.93. Multiple statistical analyses, including paired t-tests, effect sizes, and 95% confidence intervals, verified the reliability of the model selection and confirmed that the soft voting algorithm significantly outperformed the other comparison schemes. Machine learning can efficiently interpret dynamic biceps brachii sEMG signals; the proposed method improves the accuracy of fatigue recognition and provides an objective, quantitative fatigue reference index for upper limb rehabilitation training. It holds promise for assisting the dynamic adjustment of rehabilitation training intensity, thereby potentially reducing the risk of overtraining injuries and enhancing the safety of rehabilitation training.