Lower limb exoskeletons have been used in clinic to alleviate the drop foot symptom, where the joint torques for exoskeleton control significantly affect the systematic performance. However, how to design suitable assistive torques have not been well addressed. In this study, methods for joint torque generation and real-time regulation have been proposed. Firstly, a collaborative optimization method was proposed to generate torque curves. By optimizing simultaneously the muscle activations of the designed patient model and output torques of the exoskeleton model, the patient model can walk naturally with assistance. The optimized torques were used as the initial curve for further optimization during real implements. Secondly, comprehensive gait symmetry indices were designed, and a human-in-the-loop optimization algorithm was developed to online regulate the torque curve, by which individual assistance can be realized and optimized torque curves can be obtained rapidly. Thirdly, simulation and actual experiments were implemented utilizing an ankle exoskeleton. Seven hemiplegic patients were recruited in the actual experiment to sequentially execute walking in no exoskeleton, default torque, optimized torque, and zero torque modes. Experiment results showed that, gait symmetry and muscle activations can be significantly improved using optimized torque curves, and more symmetrical and coordinated walking can be achieved in five minutes.

Fast and accurate segmentation of three-dimensional vasculature significantly enhances the precision and safety of endovascular surgeries. However, current 3D segmentation methods suffer from low accuracy due to a lack of global information and severe time consumption, making them impractical for clinical scenarios. In this paper, we propose a novel Dynamic Anatomy-aware Inference (DAI) framework, which leverages the anatomical prior of vascular structures to facilitate segmentation performance. In this framework, a Target Window Search (TWS) method is proposed to sample potential patches containing target vasculature, which leads to less computational burden and more consistent input data distribution for the embedded segmentation models. Then, a Spatial Fuse Module (SFM) is designed to encode the features of sampled patches based on their topological relationships, so that the long-range dependencies of target vasculature are effectively captured. Furthermore, comprehensive experiments are conducted to validate the effectiveness of proposed methods. Compared with the prevalent sliding window inference framework, a variety of models embedded in DAI achieve significant improvements in terms of both efficiency and accuracy: 13-147 × reductions in inference time (↓), 2-9% increases in Dice (↑), 4-16% increases in mIoU (↑), 57-83% decreases in HD (↓), 21-56% increases in clDice (↑).

Motor skills are performed through sequential movements rather than isolated actions. Yet, decoding these sequences from biosignals poses a significant challenge. To address this gap, this study transitions motor decoding from classifying movements in isolated time windows to segmenting sequential movements. The proposed algorithm segments the electromyography (EMG) sequence in a coarse-to-fine manner. It begins with frame-level segmentation and locating the approximate boundaries at the movement-level. A region-growing-inspired fusion strategy is then designed to incorporate the coarse segmentation and localization results for the fined output. Experiments on a self-collected EMG dataset demonstrate impressive results in segmenting movements for participant-dependent/independent setups (accuracy:$94.2\hbox{\%}/74.7\hbox{\%}$; dice coefficient:$92.5\hbox{\%}/61.7\hbox{\%}$; mean Intersection over Union:$80.9\hbox{\%}/51.9\hbox{\%}$). Further analysis shows the algorithm's ability to capture the natural rhythm in participants' movement sequences. This research paves the way for a deep understanding of motor sequences, which benefits various applications, such as rehabilitation engineering.

Physical human–robot interaction performance of present rehabilitation robots are still not satisfactory in the clinical practice. Especially, the work space where the robot can be driven smoothly by users is still very limited, which prevents rehabilitation robots from being applied successfully. In this study, a new concept of drivable space is proposed to evaluate the work spaces of rehabilitation robots, and a method for expanding the drivable space is designed based on the dynamics of the coupled human–robot system and human joint characteristics. First, the definition of drivable space is presented based on comparison of human joint torques, and the minimal torques necessary to drive robot joints, which is mainly determined by the torque estimation errors for general rehabilitation robots driven smoothly by motors. Therefore, a method for improving torque estimation accuracies based on dynamics modeling is then designed. A data-driven error prediction method based on Gaussian process regression is proposed to adaptively compensate the model errors, by which the most accurate dynamic model so far for the coupled system can be obtained, and a method for generation of the training dataset, which is used in error prediction, is designed as well. Moreover, the torque–angle relationship of human joints is modeled and used to optimize the torque error distribution, by which it can be proven that the drivable space can be further expanded. Finally, performance of the proposed methods are demonstrated and validated by experiments carried out on a lower limb rehabilitation robot.

Active rehabilitation training can promote the neural reorganization and facilitate the rehabilitation of paralyzed patients. To provide safe and efficient active training based on rehabilitation robots, human motion intention should be recognized firstly, which can be implemented by prediction of human joint angles using sEMG. In this study, a novel CNN-LSTM model using multi-band sEMG fused with historical angles is proposed to improve the angle prediction accuracy. Eight models using sEMG signals of different numbers of frequency bands (1, 3, 5, 7) and fused or not fused with historical angles are designed and tested based on 10 subjects. The results show that, sEMG signals of suitable number of frequency bands can efficiently raise the prediction accuracy, and adding historical angles to the inputs can effectively eliminate the fluctuation of angle prediction and significantly improve the prediction accuracy. In particular, the average prediction error for the model based on 5-band sEMG and historical angles on the test data set is 0.784 degrees, which is accurate enough for practical application for the robot assisted rehabilitation.

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.

Both motor and cognitive function rehabilitation benefits can be improved significantly by patients' active participation. However, post-stroke patients, especially with attention-deficit disorders, can hardly engage in training for a longer time. In order to improve patients' attention focused on the training, an attention regulation system based on the brain-machine interface (BCI) and multimodal feedback is proposed for post-stroke lower limb rehabilitation. First, an interactive speed-tracking riding game is designed to increase the training challenge and patients' neural engagement. The character's riding speed, which is synchronized with patients' actual cycling speed, is displayed on the screen in real time. And patients' attention can further be enhanced when they try their best to track the reference speed curve. Second, an attention classifier is designed and trained by using subjects' EEG signals, which are acquired if they are tracking the reference speed curve or not. This classifier is finally applied to monitor subject's attention. If the subject is recognized with inadequate attention, sharp voice (auditory feedback) and red screen (visual feedback) will be given by the designed game to remind the subject to focus on the training. The contrast experiment results show that subjects' performance indicated by speed tracking accuracy and muscle activation can be improved significantly by using the attention regulation system. Moreover, the phenomenon of prominent decrease in theta rhythm and increase in beta rhythm can be found, which is consistent with previous research and further validates the feasibility of the proposed system in attention enhancement.

Interventional therapy for coronary bifurcation lesion has always been an intractable problem in percutaneous coronary intervention (PCI). Dual-guidewire detection can greatly assist physicians in interventional therapy of bifurcated lesions. Nevertheless, this task often comes with the challenges of X-ray images with low signal noise ratio (SNR) as well as the thinner structure of the guidewire compared to other interventional tools. In this paper, a fully automatic detection method based on an improved U-Net and the modified focal loss is proposed for dual-guidewire segmentation in 2D X-ray fluoroscopy, which accomplishes accurate and robust segmentation. The main contributions of this paper are twofold: (1) the proposed method not only addresses the extreme foreground-background class imbalance generated by the slender guidewire structure, but also solve the problem of misclassified examples caused by the guidewire-like structures and contrast agents; (2) the running speed is about 8 frames per second, which reaches near-real-time processing speed. Furthermore, data augmentation algorithm and transfer learning are used to further improve the performance. The proposed method was verified on clinical 2D X-ray image sequences of 30 patients, in which F1-score reached 0.932. The experiment results indicated that our approach is promising for assisting bifurcation lesion surgery.