Motor imagery (MI) classification in rehabilitation brain-computer interfaces (RBCIs) faces significant challenges due to the variability of electroencephalography (EEG) signals across subjects. Existing methods typically require extensive EEG data collection from each new subject, which is time-consuming and results in poor user experience. To address this issue, this paper decompose MI-EEG into subject-specific private components and shared components common across all subjects, and propose a plug-and-play domain fusion adaptive method (PPMDFA) to handle variability between subjects. In the training phase, PPMDFA introduces a Multi-Domain Fusion Graph Convolutional Network (MDFGCN) module to extract shared and private features from the MI processes of source domain subjects. In the calibration phase, the method constructs private classifiers for the target new subject using the extracted shared features combined with a small amount of labeled data. During testing, PPMDFA leverages the similarity of private components to utilize knowledge from source subjects, thereby enhancing classification accuracy for target subjects' MI. We validated the proposed method on the PhysioNet and LLMBCImotion datasets. Experimental results show that PPMDFA achieves state-of-the-art classification accuracy on both datasets, with rapid adaptation to new subjects using only 20% of the data, reaching accuracies of 73.33% and 61.62%, demonstrating strong generalization ability and robustness.

The quantitative evaluation of the improvement of physical function is crucial for patients with impaired motor function, such as stroke, in conducting related rehabilitation training activities. Specially, a practical and easy-to-operate gait feature detection and extraction system for a home is urgently needed. In this study, a home gait feature extraction method based on the inertial measurement unit is proposed. The subjects’ walking distance and speed are calculated using the double integral and the number of strides is calculated using the local maximum peak approach, while the stance phase and swing phase are calculated using the local trough approach. The compared result shows that the average walking distance accuracy is about 91.32 % and the average stride accuracy is about 96.55%. The proportion of the stance period (59.01%) and swing period (40.99%) estimated by the inertial measurement unit is close to the ratio of the two at normal speed. The experimental results demonstrate that the great accuracy of the gait spatio-temporal features is retrieved. The proposed method facilitates gait evaluation in clinics and at home, including the extraction of gait features and real-time evaluation.

Lower limb exoskeletons (LLEs) play a crucial role in assisting paraplegic patients with walking in outdoor environments characterized by complex terrains, including various stairs, slopes, and uneven grounds. However, most existing control methods for LLEs rely on predefined joint angles, lacking the flexibility to adapt to diverse terrains. This deficiency often leads to unexpected contacts between the feet of the LLEs and the ground, thereby disrupting the walking balance of the LLEs. In this paper, a novel force-sensor-free contact estimation method is proposed to tackle this problem. This method utilizes only the sensors already present on the LLEs, eliminating the need for any additional force sensors. The proposed approach is founded on the probabilistic modeling of gait phases, knee joint torques, foot heights, and the displacement of the center of mass. Moreover, Kalman filtering is employed to enhance the contact estimation accuracy by integrating multiple probabilistic models. Experiments were carried out on both robot simulation platforms and real exoskeleton robots. The experimental results demonstrate that the proposed approach can accurately estimate contacts during walking on flat ground and stairs. Specifically, it achieves an accuracy of 99% with a time deviation of 8 ms on the flat ground and an accuracy of 95% with a time deviation of 10 ms on stairs.

Lower Limb Exoskeletons (LLE) have received considerable interest in strength augmentation, rehabilitation, and walking assistance scenarios. For strength augmentation, LLE is expected to have the capability of reducing metabolic energy. However, the energy for adjusting Center of Gravity (CoG) is a main part of the total energy consumed during walking. This paper proposes a novel Human-exoskeleton Cooperative Balance (HCB) strategy which gives assistive torques balance ability and combine with the direction selected by the pilot to achieve balance walking of human-exoskeleton systems. In which, a Dynamic Torque Primitive Model (DTPM) is designed to plan a bionic assistive torque, and the balance parameters obtained by an Inverted Pendulum Model (IPM) is superimposed on it. Finally, the performance improved by the HCB strategy can break the limitation of traditional strategies and substantially increase the efficiency of assistance. We demonstrated the effectiveness of the proposed HCB strategy on the HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HCB is more efficient than traditional strategies.