This article investigates the system modeling problem for the dynamical process of human brain activity in human-robot cognitive interaction (HRCI). An important novelty of the proposed approaches is to build a computational model of a human-distributed robot-lumped parameter system (HDRLPS) that describes the inherent dynamical principle of human brain activity (with spatiotemporal-varying characteristic) undergoing the interaction between the intrinsic cognitive dynamics and extrinsic robot stimuli. A deterministic learning (DL)-based spatiotemporal dynamics identification scheme is proposed to accurately identify the spatiotemporal dynamics of HDRLS and obtain the associated knowledge as a constant radial basis functional neural network (RBF NN) model. A spatiotemporal dynamics estimator is designed with this model, which can accurately evaluate and monitor the dynamical process of human brain activity in real-time HRCI by the generated dynamics-synchronized state. The effectiveness and practicability of the approaches in the dynamics identification and evaluation for the human brain activity in HRCI are validated by the thorough analysis, including the mathematical proof, the simulation study, and the brain-computer interface (BCI) experiment using publicly available datasets. Our method is compared with state-of-the-art (SOTA) methods, such as LGGNet, EEGNet, Tsception, EEG-Deformer, EEG-Transformer, and EEGViT. The results show that our method can outperform these methods with better recognition accuracy and macro- $F1$ scores. The source code can be found at: https://github.com/alonexing/source_code/tree/master.

Magnetic resonance imaging (MRI) is highly important for diagnosing knee injuries because of its ability to provide detailed images. However, the key problem in intelligent MRI diagnosis is how to identify and extract the most relevant features from a large amount of data. In this paper, we present a single training stage method. This method can extract features from images, sequences, and anatomical planes at different levels simultaneously. First, we propose a nested Mamba module. This module allows parallel extraction of image-level and sequence-level features from the same anatomical plane in MRI. Second, an anatomical-level Mamba module is designed. It integrates features extracted from different anatomical planes. Finally, self-distillation is applied to improve the model’s feature extraction efficiency. Experimental results show that our proposed model outperforms sub-optimal models by 3.7% in accuracy and 2.4% in F1-score. Moreover, compared with the multi-stage model, the single-stage multi-class classification model greatly improves classification efficiency. In conclusion, the model developed in this paper can effectively and accurately conduct intelligent knee MRI diagnosis.

Focus of attention is one of the most influential factors facilitating motor training performance. Most of robotic training methods have not well solved the negative effect of divided-attention on motor execution performance, resulting in limited rehabilitation efficiency for motor-cognitive dysfunction. In this study, we propose a novel visuomotor human-robot interaction framework by integrating a gaze-visual game and force-movement robot, to realize more efficient training for both attentional and motor function. An important novelty of this framework is to design a dynamical pattern recognition scheme for the hierarchical-coupled behavior of attentional and motor execution, to facilitate efficient human-robot interaction in both cognitive and motor perspectives. Specifically, an attentional-motor dynamical system modeling method is first developed by using the gaze, force and movement data collected from the human under different attentional-motor behavior. Then, an online dynamical pattern recognition scheme can be design with these models to online recognizing the human’s attentional and motor behavior states. The training robot system can dynamically adjust the parameters according to the recognition results, to guide the collaboration of both attentional and motor training. Experimental study are conducted to demonstrate the desired accuracy and efficiency of our designed approaches in attentional-motor behavior recognition and training.

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.

Brain-computer interface (BCI) is an important technology in developing the closed-loop brain training system for cognitive functional rehabilitation. Most of existing BCI paradigms have not ensured desired immersiveness of mind and body, thereby limiting participants’ engagement in training tasks. In this paper, we propose a sensory-immersive BCI paradigm for decision-making with a novel motion-panoramic virtual-reality system, aiming for deep involvement of both mind and body in brain functional training. This paradigm integrates visual, auditory and motion multi-sensory stimulation by using the Gait Real-time Analysis Interactive Lab system to implement the modified ultimatum game for decision making. The designed paradigm is validated through three experimental studies, including the event-related potentials analysis, power spectral density analysis and the brain network analysis. They demonstrate that the designed paradigm can achieve better performance in motor-cognitive interaction and multi-sensory coordination, by effectively enhancing brain activation in visual, auditory, and motor processing regions, which can result in more effective activation of decision-making areas like the prefrontal cortex. Compared to the existing paradigm, our paradigm can increase the number of high-intensity functional connections in the brain regions of participants by 62.8% (from 86 to 140), and the number of effective functional connections increased by 90.5% (from 252 to 480).

Object pose estimation has extensive applications in various industrial scenarios. However, the heavy reliance on dense 6-D annotation and textured object models has become a significant obstacle to the widespread industrial application of 6-D object pose estimation methods. In this work, we presentSS-Pose, a self-supervised learning framework for estimating 6-D object poses without annotated 6-D data and textured model.SS-Poseproposes thecoordinate system datum reinitializerstage to dynamically establish a sequence-level pose representation datum, and thetemporal–spatial constraint resolvermodule to obtain the self-supervised learning target through interframe constraints. We introduce a one-shotcross-coordinate transformationthat establishes the relationship between the 6-D representation and the object poses, which can be further utilized in real-world tasks. We evaluated the proposedSS-Poseon the challenging YCB-Video dataset and texture-less T-LESS dataset. Our approach achieves competitive performance with significantly lower data dependency, making it suitable for visual perception in industrial applications.

This article investigates the problem of small fault detection (sFD) for discrete-time nonlinear systems with uncertain dynamics. The faults are considered to be "small" in the sense that the system trajectories in the faulty mode always remain close to those in the normal mode, and the magnitude of fault can be smaller than that of the system's uncertain dynamics. A novel adaptive dynamics learning-based sFD framework is proposed. Specifically, an adaptive dynamics learning approach using radial basis function neural networks (RBF NNs) is first developed to achieve locally accurate identification of the system uncertain dynamics, where the obtained knowledge can be stored and represented in terms of constant RBF NNs. Based on this, a novel residual system is designed by incorporating a newmechanism of absolute measurement of system dynamics changes induced by small faults. An adaptive threshold is then developed for real-time sFD decision making. Rigorous analysis is performed to derive the detectability condition and the analytical upper bound for sFD time. Simulation studies, including an application to a three-tank benchmark engineering system, are conducted to demonstrate the effectiveness and advantages of the proposed approach.