Lower extremity robotic exoskeletons (LEEX) can not only improve the ability of the human body but also provide healing treatment for people with lower extremity dysfunction. There are a wide range of application needs and development prospects in the military, industry, medical treatment, consumption and other fields, which has aroused widespread concern in society. This paper attempts to review LEEX technical development. First, the history of LEEX is briefly traced. Second, based on existing research, LEEX is classified according to auxiliary body parts, structural forms, functions and fields, and typical LEEX prototypes and products are introduced. Then, the latest key technologies are analyzed and summarized, and the research contents, such as bionic structure and driving characteristics, human–robot interaction (HRI) and intent-awareness, intelligent control strategy, and evaluation method of power-assisted walking efficiency, are described in detail. Finally, existing LEEX problems and challenges are analyzed, a future development trend is proposed, and a multidisciplinary development direction of the key technology is provided.

Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.

This work aims to estimate a high-quality depth map from a single RGB image. Due to the lack of depth clues, making full use of the long-range correlation and local information is critical for accurate depth estimation. To this end, we introduce an uncertainty rectified cross-distillation between the Transformer and convolutional neural network (CNN) to achieve a comprehensive depth estimator. Specifically, we utilize the depth estimates from the Transformer branch and CNN branch as pseudo labels to teach each other. At the same time, the pixel-wise depth uncertainty is modeled to mitigate the negative impact of noisy pseudo labels. To avoid the large capacity gap induced by the strong Transformer branch deteriorating the cross-distillation, we transfer the feature maps from the Transformer to the CNN and develop coupling units to assist the weak CNN branch in leveraging the transferred features. Furthermore, we introduce CutFlip, a surprisingly simple yet highly effective data augmentation technique, which forces the model to focus on more valuable depth reasoning clues apart from the vertical image position. Extensive experiments demonstrate that our model, termed URCDC-Depth , exceeds in performance previous state-of-the-art approaches on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets, with no additional computational burden in the evaluation phase. The source code will be publicly available upon acceptance.

Gesture recognition via surface electromyography (sEMG) has drawn significant attention in the field of human–computer interaction. An important factor limiting the performance of sEMG-based pattern recognition (PR) is the generalization ability which sEMG changes for the identical movements when conducted at various positions or by different persons. Thus, this study aims to explore the generalization of classifier to develop a stable classification model that does not require relearning, even if it is used by other people. We propose a new feature extraction method to diminish the influence of limb position on sEMG-based PR. Specifically, the sEMG features are extracted directly from time domain. This condition is accomplished by using Fourier transform properties, difference, and the sum of squares differences. The best offline cross-validation accuracy (CVA) results are 88.775% training data from the tenth subject and testing data from the fifth subject in the NinaPro dataset. The best online CVA is 99%, and the movement selection time is 47.036 ± 1.028 ms. In comparison with the well-known sEMG feature, the CVA and the generalization of the proposed features improved substantially. These improvements aim to facilitate the practical implementation of myoelectric interfaces.

In this paper, the design, modeling and control of a novel morphing quadrotor are presented. The morphing quadrotor can fly stably and accurately in the air while simultaneously undergoing shape transformation, regardless of the asymmetry of the model. The four arms can rotate around hinges on the main body of the quadrotor to form various topological models. The arms are not in the same plane, so they can overlap with each other. In the extreme case, the width of the morphing quadrotor can be reduced to the diameter of a single rotor to allow the quadrotor to fly through narrow gaps more easily. Reinforcement learning (RL) with an extended-state approach is introduced in this paper to optimize the attitude control law and enable automatic adaptation to model changes. A deterministic policy gradient (DPG) algorithm based on an actor-critic structure with four neural networks in a model-free approach is used to train the controller. Finally, a linear programming method named fast simplex algorithm is presented to solve the control allocation problem of morphing quadrotors in real time with affordable computational cost in this paper. The controller has been tested on our real morphing quadrotor platform and achieves excellent flight performance.

Over the past few years, self-supervised monocular depth estimation has received widespread attention. Most efforts focus on designing different types of network architectures and loss functions or handling edge cases, for example, occlusion and dynamic objects. In this work, we take another path and propose a novel conditional diffusion-based generative framework for self-supervised monocular depth estimation, dubbed MonoDiffusion. Because the depth ground-truth is unavailable in a self-supervised setting, we develop a new pseudo ground-truth diffusion process to assist the diffusion for training. Instead of diffusing at a fixed high resolution, we perform diffusion in a coarse-to-fine manner that allows for faster inference time without sacrificing accuracy or even better accuracy. Furthermore, we develop a simple yet effective contrastive depth reconstruction mechanism to enhance the denoising ability of model. It is worth noting that the proposed MonoDiffusion has the property of naturally acquiring the depth uncertainty that is essential to be implemented in safety-critical cases. Extensive experiments on the KITTI, Make3D and DIML datasets indicate that our MonoDiffusion outperforms prior state-of-the-art self-supervised competitors. The source code will be publicly available upon the acceptance.