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Decoding lower-limb motor imagery (MI) is highly important in brain–computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified. [ABSTRACT FROM AUTHOR]

Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model. [ABSTRACT FROM AUTHOR]

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human–machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified. [ABSTRACT FROM AUTHOR]

The integration of diverse weaving angles in vascular stent design proves beneficial for addressing complex applications such as bending, branching, and load-bearing. However, the weaving process may introduce frictional corrosion issues among threads, necessitating further investigation into frictional behaviors at different weaving angles. This study simplifies the contact between nickel-titanium (NiTi) alloy threads in woven stents as line-to-line contact between rods. A proposed in vitro experiment, based on weaving angles of 30°, 45°, 60°, 75°, and 90°, systematically analyzes friction and wear characteristics, including friction coefficients, wear parameters, and morphology. Findings indicate significant impacts of weaving angles on tribological behaviors, with 30° weaving angle stents exhibiting the lowest wear rate. The study identifies four developmental stages in the life cycle of line-to-line contact wear: breaking-in wear, steady wear, severe wear, and post-stable wear. Under 25% calf serum lubrication, lower friction coefficients and wear rates are observed, validating NiTi alloy suitability for lubricated human environments. The investigation reveals a nonlinear correlation between weaving angles, friction cycles, and wear rates, offering insights for the tribological design of woven instruments. [ABSTRACT FROM AUTHOR]

To improve the trajectory tracking performance of planar motors against disturbances, model predictive position control (MPPC) methods using the non‐linear disturbance observer (NDO) are proposed in this study. Based on the single‐axis dynamic model with disturbances, a single‐axis NDO is designed using an extended state observer approach. The designed NDO is expressed as a third‐order non‐linear state‐space equation in which the position error, velocity error, and lumped disturbance in the single axis are taken as the state variables. Two MPPC methods are developed based on the NDO. In the first MPPC, the disturbance is embedded into the prediction model using the NDO, and a controller is designed to minimise a quadratic cost function, which is established by applying the prediction model with disturbance. The output of the controller is the control action. In the second MPPC, a controller is used to minimise the quadratic cost function, which is built by employing the prediction model without disturbance. The sum of the output of the controller and the compensated disturbance estimated by the NDO is the control action. The comparative experiment is performed on a planar motor system self‐developed in the laboratory. The effectiveness of the proposed methods is verified via the experimental results. [ABSTRACT FROM AUTHOR]