Publications

Stroke patients often experience hand dysfunction, requiring effective assistance. While cable-driven wearable robots have advanced, challenges persist in anchor ring design and actuation system. Specifically, a trade-off exists between material comfort and anchor point stability, as well as between actuator versatility and control simplicity. To address these challenges, this article proposes an adjustable rigid-soft wearable hand robot with a magnetorheological cable-driven mechanism. The adjustable rigid-soft coupling ring enhances both comfort and stability, demonstrating significantly improved mechanical performance compared to both the rigid-soft ring and the silicone ring of the same size. The wearable structure integrates the proposed adjustable anchor ring and a ripple strap design for better adaptability. Additionally, the magnetorheological actuation system balances versatility and control simplicity, enabling coordinated motion, adaptive grasping, independent finger control, and mechanical safety by selectively activating specific magnetorheological fluid. Finally, the proposed system's performance and effectiveness in assisting grasping have been confirmed through experiments.

Credit assignment is a critical problem in cooperative Multi-Agent Reinforcement Learning (MARL). To address this problem, current studies mainly rely on the intrinsic reward, which is directly summed with the global reward to generate a total reward. However, such kinds of intrinsic reward functions ignore the dependence among agents and inevitably limit the adaptivity and effectiveness of MARL methods. In this paper, we propose a novel method, Attention-based Intrinsic Reward Mixing Network (AIRMN), for credit assignment in MARL. Specifically, we design a new intrinsic reward network on the basis of the attention mechanism, in order to enhance the effectiveness of teamwork. Besides, we devise a new mixing network that combines the intrinsic and extrinsic rewards in a nonlinear and dynamic manner, so as to adapt the total reward to the variation of the environment. Experimental results on the battle games of StarCraft II demonstrate that AIRMN outperforms the state-of-the-art methods in terms of the average test win rate, and also validate that AIRMN can dynamically return the precise intrinsic reward to each agent based on their contributions to the team cooperation, thereby better dealing with the credit assignment problem.

Electroencephalogram (EEG)-based emotion recognition has gradually become a research hotspot. However, the large distribution differences of EEG signals across subjects make the current research stuck in a dilemma. To resolve this problem, in this article, we propose a novel and effective method, Multi-Source Feature Representation and Alignment Network (MS-FRAN). The effectiveness of proposed method mainly comes from three new modules: Wide Feature Extractor (WFE) for feature learning, Random Matching Operation (RMO) for model training, and Top- h ranked domain classifier selection (TOP) for emotion classification. MS-FRAN is not only effective in aligning the distributions of each pair of source and target domains, but also capable of reducing the distributional differences among the multiple source domains. Experimental results on the public benchmark datasets SEED and DEAP have demonstrated the advantage of our method over the related competitive approaches for cross-subject EEG-based emotion recognition.

Remote manipulation of a robot without assistance in an unstructured environment is a challenging task for operators. In this paper, a novel methodology for haptic constraints in a point cloud augmented virtual reality environment is proposed to address this human operation limitation. The proposed method generates haptic constraints in real time for an unstructured environment, including regional constraints and guidance constraints. A modified implicit surface method is applied for regional constraint generation for the entire point cloud. Additionally, the isosurface derived from the implicit surface is proposed for real-time three-dimensional artificial force field estimation. For guidance constraint generation, a new incremental prediction and local artificial force field generation method based on the modified sigmoid model is proposed in an unstructured point cloud virtual reality environment. With the generated haptic constraints, the operator can control the robot to realize obstacle avoidance and easily reach the target tasks. System evaluation is conducted, and the result demonstrates the effectiveness of the proposed method. In addition, a 10-participant study with users who control the robot to three specific targets shows that the system can enhance human operation efficiency and reduce time costs by at least 59% compared with no-haptic-constraint operations. Additionally, the designed questionnaire also demonstrates that the proposed methodology can reduce the workload during human operations.

Given the shortcomings of subjectivity and destructiveness associated with traditional methods of measuring food texture parameters (such as fruit firmness and meat tenderness) when using a texture meter, a non-contact measurement method utilizing an Air-puff combined with Structured Light Imaging (ASLI) was proposed in this study. First, the finite element simulation method was used to simulate the airflow impact model and optimize the ranges of the angle and distance parameters. The detection device was then developed, and binocular 3D phenotype acquisition and 3D feature extraction algorithms were investigated. Denoising, point cloud segmentation, greedy projection triangulation, Delaunay triangulation, and surface fitting algorithms were used to process the point cloud, and concave parameters such as depth, mapping area, surface area, and volume of the concave area on the beef surface were obtained. Finally, four typical samples with different meat (beef and chicken) and fruit (kiwifruit and peach) parameters were tested. Five machine learning methods were used to model and analyze the correlation among texture parameters. The results revealed that the model established by the ensemble learning modeling method with extracted concave parameters possessed the highest accuracy. The prediction accuracy of the shear force values of the beef and chicken samples were 0.92 and 0.91. The accuracy of meat and fruit grading was 0.942 and 1.0. Therefore, the non-contact detection method proposed in this study to determine food texture characteristics based on airflow can be used to replace the traditional texture analyzer in food engineering and exhibits good application potential.

Based on the established serial 6-DOF robot calibration experiment platform, this paper aims to analyze and compare the effects of four error compensation methods, which are pseudotarget iteration-based error compensation method with three different forms and the Newton–Raphson-based error compensation method. Firstly, the pose error model of the serial robot is established based on the M-DH model in this paper. The calibration results show that the accuracy of the Staubli TX60 robot has been greatly improved. The average comprehensive position accuracy is increased by 88.7%, and the average comprehensive attitude accuracy is increased by 56.6%. Secondly, the principles of the four error compensation methods are discussed, and the effectiveness of the four error compensation methods are compared through experiments. The results show that the four error compensation methods can achieve error compensation well. The compensation accuracy is consistent with the identification accuracy of the kinematic model. The pseudotarget iteration with differential form has the best performance by the comprehensive consideration of accuracy and computational efficiency. Error compensation determines whether the accuracy of the identified model can be achieved. This paper presents a systematic experimental validation research on the effectiveness of four error compensation methods, which provides a reliable reference for the kinematic error compensation of industrial robots.

The proposed ESD method combining SE with SVs and SS can serve as an assistive diagnostic tool to help medical professionals and individuals detect and prevent strokes at an earlier stage, reduce workload, and improve identification objectivity. The code and experimental protocol of this paper are available at https://github.com/LiuYingchenseu/ESPVC.

A versatile leg-wheel robot called STransleg is proposed in this paper. STransleg is propelled by four identical leg-wheel mechanisms, which can transform between the legged and wheeled modes. The leg has three active joints in the legged mode, and the wheel is active in the wheeled mode. The waist yaw and pitch joints are introduced to STransleg to improve the locomotion flexibility and stability. In this paper, the design and kinematics modeling methods of STransleg are described. Additionally, the control method is proposed for STransleg to make it do the four-wheeled locomotion, walking locomotion, and carriage-like locomotion. The simulations are carried out to make the dynamical model of STransleg do the different locomotion, and the results verify the feasibility of the robot design method and the proposed control method.

Estimating wrist movements through neural drives is crucial in human-machine interface (HMI). However, studies on wrist movements mostly focused on isometric contractions, while research on dynamic EMG decomposition during non-stationary movements is notably scarce. Moreover, the impact of different resistance on the motor unit (MU) decomposition and wrist angle estimation remains unexplored. To address these gaps, this paper proposed a novel framework to decode neural drives from EMG signals during dynamic wrist movements. Specifically, the EMG signals were divided into short segments firstly. Next, progressive FastICA peel-off (PFP) algorithm was utilized to decompose each EMG segment into motor unit spike trains (MUST). Then, a linear window function was applied to track the motor units (MU) to obtain complete MUSTs. Three resistance levels were investigated during wrist flexion and extension: 20%, 40%, and 60% maximum voluntary contraction (MVC). Multiple linear regression (LR) and convolutional neural network (CNN) were used to estimate wrist angles within a range of ± 20° based on neural drives. Results showed the proposed framework could effectively identify MUs at these three resistance levels, with an average global pulse-to-noise ratio (PNR) above 20 dB. The determination coefficients of LR model were 0.92 ± 0.06, 0.91 ± 0.07, and 0.85 ± 0.13 at the three resistance levels, respectively, while those of CNN were 0.88 ± 0.10, 0.88 ± 0.11, and 0.81 ± 0.17. This study demonstrates it is feasible to estimate wrist angles based on decomposed neural drives at different resistance levels, and has significant implications for HMI development.

Stroke survivors often suffer from severe discomfort caused by persistent hand edema, including hand pain, restricted knuckle mobility, and negative emotions, which significantly hinders normal rehabilitation progress. Whether physical or psychological in impact, the prevention and treatment of hand edema warrant significant attention throughout all stages of stroke rehabilitation. However, traditional rehabilitation therapies generally neglect edema management, focusing primarily on motor function recovery. Consequently, this study introduces the TESSR (ThErmal Stimulation device for Stroke Rehabilitation), a novel apparatus designed to promote hand rehabilitation for stroke survivors. TESSR is equipped with a flexible stimulation array comprising 50 independently controlled stimulation units, each capable of rapid heating and cooling transitions as well as delivering sustained and stable thermal outputs. This alternating thermal stimulation (TS) is designed to alleviate edema and activate the sensory cortex, thereby facilitating neuroplasticity. A controlled clinical study was conducted with 12 stroke survivors to assess the effectiveness of the TESSR device. The participants were divided into two groups: one receiving conventional rehabilitation (CR group) and the other receiving supplementary TS conducted by the TESSR device (TS group). Following a two-week intervention, the TS group exhibited a notable reduction in hand edema, with a mean decrease of 38.6%, compared to the minimal improvement observed in the CR group. Furthermore, participants in the TS group demonstrated improved hand function, including enhanced sensory perception and diminished pain. The findings demonstrate the potential of TESSR as a comprehensive and effective tool for hand rehabilitation.