Publications

Review: the development of tactile feedback devices

With the advent of the holographic communication era, the amount and types of information continue to increase. People urgently need to explore new methods of information interaction. Tactile interaction technology can produce tactile feedback, greatly enhancing multimedia interactivity and user immersion, and it is an indispensable key technology in future multisensory interactive communication. This paper will introduce the latest research progress of tactile feedback devices in the past 5 years. According to different installation methods, it will be divided into four types and will summarize the characteristics of the most representative tactile feedback devices in the past 5 years in a table. Subsequently, the paper will summarize the existing problems of each type of tactile feedback device and analyze the future development trends of this field. The application of haptic feedback technology is prospected in the end.

Phase Synchronization Information for Classifying Motor Imagery EEG From the Same Limb

The classification of motor imagery Electroencephalogram (EEG) of the same limb is important for natural control of neuroprosthesis. Due to the close spatial representations on the motor cortex area of the brain, the discrimination of the different motor imagery tasks is challenging. In this paper, phase synchronization information was proposed to classify motor imagery EEG within the same limb. In addition, non-portable was compared with portable EEG acquisition equipment for the purpose of making the brain computer interface (BCI) system more practical. In the non-portable case, the average accuracy of the binary classification and 3-class classification was 60.6% and 42.7%. In the portable case, the average EEG decoding accuracy of 58.5% and 39.9% was achieved for the two and three tasks. Furthermore, in both two cases, different sets of electrode pairs got the similar results. Moreover, we found that the proposed phase information based method was less sensitive to the number of EEG channels and had less performance degradation in portable EEG equipment. These results show it is possible to use phase synchronization information to discriminate different motor imagery tasks within the same limb. Eventually, this will potentially make the control of neuroprosthesis or other rehabilitation device more natural and intuitive.

Development and Evaluation of a Quasi-Passive Stiffness Display

Learning Multimodal Confidence for Intention Recognition in Human-Robot Interaction

The rapid development of collaborative robotics has provided a new possibility of helping the elderly who has difficulties in daily life, allowing robots to operate according to specific intentions. However, efficient human-robot cooperation requires natural, accurate and reliable intention recognition in shared environments. The current paramount challenge for this is reducing the uncertainty of multimodal fused intention to be recognized and reasoning adaptively a more reliable result despite current interactive condition. In this work we propose a novel learning-based multimodal fusion framework Batch Multimodal Confidence Learning for Opinion Pool (BMCLOP). Our approach combines Bayesian multimodal fusion method and batch confidence learning algorithm to improve accuracy, uncertainty reduction and success rate given the interactive condition. In particular, the generic and practical multimodal intention recognition framework can be easily extended further. Our desired assistive scenarios consider three modalities gestures, speech and gaze, all of which produce categorical distributions over all the finite intentions. The proposed method is validated with a six-DoF robot through extensive experiments and exhibits high performance compared to baselines.

Ultra-sensitive flexible resistive sensor based on modified PEDOT: PSS inspired by earthworm

Bilateral motion prediction and control for teleoperation under long time-varying delays

Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters

Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics. Based on grasp taxonomy, we selected four natural movements for our study: Large Diameter (LD), Sphere 3-Finger (SF), Precision Disk (PD), and Parallel Extension (PE), each incorporating two levels of speed and force parameters. The results demonstrate that a combination of MRCPs features and MBNs metrics can successfully decode not only the types of movements and kinematic parameters but also differentiate between different grasp taxonomy characteristics, such as the number of fingers exerting force and the type of grasp. In terms of movement type, we achieved a peak four-class accuracy of 60.56%. For grasp type and number of fingers exerting force, binary classification peak accuracies reached 79.25% and 79.28%, respectively. In the case of kinematic parameters, the Precision Disk movement exhibited the highest binary classification peak accuracy at 84.65%. Moreover, our research also found the changes and patterns in brain region connectivity across both time and frequency domains. We believe that our research highlights the potential of MBNs to enhance the functionality of Brain-Computer Interface (BCI) systems for more intuitive control mechanisms and contributes valuable insights into the brain's operational mechanisms.

Modeling and control design of a bronchoscope robotics system

For the pulmonary nodule biopsy problem, this paper investigates an intervention robotics system based on the bronchoscope. To achieve precise control of the system, the mathematical model is established at first. The system mainly consists of an inserting tube and a tube tip, which are modeled based on a moving flexible beam and a soft robot, respectively. In order to reduce the harm to the patient during the operation, the robot system should suppress the vibration of the inserting tube during the process of tracking the desired trajectory in the bronchus. Therefore, we use the boundary control algorithm to design a controller capable of suppressing vibration for the inserting tube. Meanwhile, in order for the robot system to accurately select the bronchus corresponding to the branch, we designed a soft robot controller that can bend or deflect the soft robot to a specified posture. Finally, we used theoretical proof and numerical simulation to verify the conclusion.

Design of a Passive Gravity Compensation Mechanism for Wearable Bilateral Lower Limb Exoskeletons

A Novel Soft Glove Utilizing Honeycomb Pneumatic Actuators (HPAs) for Assisting Activities of Daily Living

Fabric-based pneumatic actuators (FPAs) are extensively employed in the design of lightweight and compliant soft wearable assistive gloves. However, conventional FPAs typically exhibit limited output force, thereby restricting the applications of such gloves. This paper presents the development of a novel honeycomb pneumatic actuator (HPA) constructed using flexible thermoplastic polyurethane (TPU) coating through hot pressing or ultrasonic welding techniques. Compared to the previously utilized double-layer fabric-based pneumatic actuators (DLF-PAs), the HPAs yields a remarkable 862% increase in end output force. It can produce a tip force of 13.57 N at a pressure of 150 kPa. The integration of HPAs onto a soft pneumatic glove enables the facilitation of various activities of daily living. A series of trials involving nine patients were conducted to assess the effectiveness of the soft glove. The experimental results indicate that when assisted by the glove, the patients' finger metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints achieved angles of 87.67 ± 19.27° and 64.2 ± 30.66°, respectively. Additionally, the average fingertip force reached 10.16 ± 4.24 N, the average grip force reached 26.04 ± 15.08 N, and the completion rate of daily functions for the patients increased from 39% to 76%. These outcomes demonstrate that the soft glove effectively aids in finger movements and significantly enhances the patients' daily functioning.

Decentralized adaptive fuzzy output feedback control of nonlinear interconnected systems with time-varying delay

A robust adaptive output-feedback control scheme based on K-filters is proposed for a class of nonlinear interconnected time-varying delay systems with immeasurable states. It is difficult to design the controller due to the existence of the immeasurable states and the time-delay couplings among interconnected subsystems. This difficulty is overcome by use of the fuzzy system, the K-filters and the appropriate Lyapunov-Krasovskii functional. Based on Lyapunov theory, the closed-loop control system is proved to be semi-global uniformly ultimately bounded (SGUUB), and the output tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the approach.

Design of multichannel data acquisition module based on FPGA

Sequential Predictors for Uncertain Euler–Lagrange Systems with Large Transmission Delays

This paper investigates the state prediction problems for uncertain Euler–Lagrange systems with large time delays during data transmissions. A set of sequential predictors is proposed to estimate the actual real-time states of the systems by using the delayed information of measurements. The arbitrarily large delays are handled by applying adequate numbers of serial sub-predictors. Meanwhile, the novel prediction structure of each subsystem is designed to deal with nonlinearities and unknown dynamics in the systems. Then, the predictor design is extended to the case without using delayed velocity measurements by updating the structure of the first sub-predictor. Sufficient conditions for the design of predictor gains, ensuring the boundness of prediction errors, are obtained through Lyapunov–Krasovskii functionals. The effectiveness and robustness of the uncertainties of the proposed method are verified by comparative results in simulations.

Joint Spotting and Recognition of Micro-Expressions in Long Videos via Temporal Representation Learning

Facial micro-expressions (MEs) are brief and subtle facial movements that reveal genuine emotions, making them critical cues for affective analysis and human-robot interaction. In this work, we propose a unified framework for micro-expression spotting and recognition in long video sequences. Our model is built upon the Perceiver IO architecture, which enables scalable temporal modeling across variable-length sequences by encoding global context into a fixed-size latent space. To simultaneously address spotting and recognition, we adopt a dual-branch decoder that estimates frame-wise expression likelihoods and emotional categories, respectively. To enhance stability and boundary sensitivity, we introduce two auxiliary learning strategies: a soft KL-divergence-based consistency loss that enforces emotion prediction coherence within expression segments, and a boundary-aware contrastive loss that sharpens temporal boundaries between expressive and neutral frames. In addition, we introduce a phased supervision scheme that leverages ground truth segments in early training and pseudolabels in later stages. Experiments conducted on CAS(ME)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and SAMM-LV demonstrate that our method achieves state-of-the-art performance.

Haptic Vibrations in Emotion Induction and Regulation: Insights from Subjective Ratings and EEG Signals

Architecture and Design of a Wearable Robotic System for Body Posture Monitoring, Correction, and Rehabilitation Assist

Bridging Human-Robot Co-Adaptation via Biofeedback for Continuous Myoelectric Control

This letter proposes a novel human-robot co-adaptation framework for robust and accurate user intent recognition, specifically in the context of automatic control in assistance robots such as neural prosthetics and rehabilitation devices empowered by electrophysiological signals. Our goal is to incorporate user adaptability early in the training phase to facilitate both machine recognition and user adaptability, rather than relying solely on brute-force machine learning methods. The proposed framework is featured by applying biofeedback-based user adaptive behavior into model training, while the machine can adapt to those changes through online learning. Specifically, this study focuses on the recognition of two-degree-of-freedom simultaneous and continuous wrist movement intentions based on surface electromyogram (sEMG) array signals, and the performance is tested on twelve able-bodied subjects. The co-adaptive evaluation experiment demonstrates the robust control of this method by introducing sEMG electrode displacement as perturbations. Experimental results show that this method improves the completion time of centre-out tasks by 13% compared to conventional methods (Cohen's d=0.637), and debias 86% of the effect of electrode shift perturbations. This study provides insights into the potential for incorporating human adaptability into machine intelligence to improve user intent recognition and automatic robot control.

Fuzzy MSD based feature extraction method for face recognition