Despite extensive investigations into the multi-user haptic-enabled robotic system (M-Hers), achieving scalable control design in the presence of non-passive human operators remains a key challenge. This is primarily due to the increasing complexity of stability conditions and interaction coupling as the number of operators grows. In this study, we address this challenge in two steps. First, we introduce the individual interaction environment (IIE) to isolate the passivity violations, which facilitates the independent control design for each human-robot subsystem, thereby enhancing the scalability with respect to the number of subsystems. Second, within the IIE framework, we identify passivity-violating components caused by partners' active behaviors and propose a novel augmented tank-based controller (ATBC) to guarantee passive IIE while maintaining high rendering accuracy. Specifically, the ATBC employs an energy-related power regulation strategy to enhance interaction safety and a time-varying control gain to mitigate the negative effects of power regulation on rendering fidelity. We validated the proposed method through collaborative haptic tasks on a customized M-Hers composed of three robots in four different scenarios. Comparative studies demonstrate that our approach effectively ensures IIE passivity in the presence of active human behaviors, while ensuring high reproducibility and achieving a favorable balance between passivity and rendering accuracy.

Actuation efficiency is a significant consideration for soft exosuits. It has important significance for reducing system weight and improving the effectiveness of walking assistance. Inspired by a fact that desired assistance from exosuits varies over the gait stage, this study developed a power modulation unit (PMU) to adapt to human walking characteristics, aiming to reduce the power requirement of exoskeletons. The underlying principles consist of: 1) dividing the gait phase into assistance period and idle period by different gait characteristics, where the terminal stance phase is defined as the assistance period and other phases are defined as the idle period; 2) motor power is stored in elastic elements during the idle period with mechanical advantage; 3) an amplified power bursts during the assistance period. Preliminary experiments were carried out on an ankle exosuit with human users. Experimental results indicate that the proposed PMU can amplify the output power by 3.1 times while affecting little on wearers’ normal gait, which implies the potential of the developed PMU for use in wearable actuation systems. Note to Practitioners—This work was motivated by the trade-off between multipath assistance and lightweight actuation. That is, multipath assistance typically necessitates multiple motors, consequently adding to the overall weight of the system. In this work, we developed a PMU to adapt to human walking characteristics (motor power is stored in elastic elements during the idle period and released when needed), aiming to reduce the requirement for peak actuation power. Experimental results show that the PMUs can provide an average power amplification ability of 3.1 times and does not interfere with normal human gait. The developed PMU holds great potential in walking-assisted exosuits or other devices for human movement assistance.

Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain-computer interface (BCI) systems. However, existing methods often face a critical trade-off between decoding accuracy and model interpretability, limiting their applicability in real-world settings. To address this challenge, we proposed the WavTSK, an end-to-end interpretable fuzzy neural network for efficient MI-EEG decoding. The WavTSK employs a deeply integrated architecture that combines a learnable wavelet-based feature extraction module with a multi-rule Takagi-Sugeno-Kang (TSK) fuzzy classifier. The feature extractor incorporates learnable wavelet filters, statistical descriptors, and an adaptive band-weighting mechanism to capture rich multi-scale time-frequency representations directly from raw EEG. The extracted representations are then processed by a TSK fuzzy reasoning layer, enabling rule-level transparency in decoding. Experiments on the four-class BCI Competition IV-2a dataset showed that WavTSK achieved mean decoding accuracies of 71.63% in cross-validation and 70.55% in the cross-session hold-out setting, consistently outperforming state-of-the-art black-box deep learning models while maintaining strong interpretability. The results highlight the potential of WavTSK as a powerful and interpretable framework for reliable EEG decoding, advancing the development of trustworthy and clinically applicable BCI technologies.

Emerging evidence suggests that prolonged virtual reality (VR) exposure may impair human sensory systems. Most research has focused on the visual, proprioceptive, and vestibular systems, but the impact of VR on haptic perception remains unclear. In this study, we investigated alterations in human sensitivity to contact force following VR gaming. A force perception task was designed to assess changes in contact force across six difficulty levels with step sizes ranging from 0.5 to 5 N. A total of 18 participants performed the task before VR, after 10 min, and after an additional 20 min of VR. The perceptual accuracy of correctly perceiving force changes at each difficulty level was measured across three test periods. The results indicated that 66.67% of participants experienced a negative impact from VR at the 1-N change step. Perceptual accuracy significantly decreased in this group, with a 9.17% reduction after 10 min and a 17.50% reduction after an additional 20 min. In contrast, minimal effects were observed in the remaining participants. These findings suggest that even short-term VR exposure can impair force discrimination in certain users, with the effects becoming more pronounced over time.

Robot-assisted ankle rehabilitation training imitating physician's professional techniques is highly important for promoting personalized training and improving clinical outcomes. In this work, we propose a two-level kernelized movement primitives (2-level-KMP) imitation learning algorithm under the kernelized movement primitives (KMP) framework, which reproduces physician's experience and optimizes the imitation trajectory during rehabilitation, to realize physician-level performance in robot-assisted ankle rehabilitation training. First, a KMP process combined with a Bayesian optimizer is used to imitate the rehabilitation trajectory. Second, the other KMP process is used to smooth the imitation trajectory further. Then the two KMP processes combined with patient-in-the-loop optimization (PILO) realize temporal rehabilitation adaptation. Finally, the 2-level-KMP algorithm is reproduced on a parallel ankle rehabilitation robot (PARR), which enables the patient's passive rehabilitation training to be empirical and adaptive. Ten ankle dysfunction patients were involved in clinical experiments, with the results showing that the proposed algorithm can accurately reproduce physician's trajectories and modulate trajectories based on patient's feedback. After ten rehabilitation exercises, the number of modulation points calculated from patient's torque feedback decreases by 85.19% on average compared with the beginning stage. A comparison between the 2-level KMP algorithm and existing algorithms shows that the 2-level-KMP algorithm can better ensure smoothness and retain the shape of the trajectory during trajectory modulation, ensuring the safety of ankle rehabilitation and retaining the experience of the physician.

Restoring the capability to perform instrumental activities of daily living (IADLs) is an imperative step towards independent living for neurologically impaired individuals. Robot-assisted task-oriented training with haptic feedback has the potential to enhance patients’ ability to perform IADLs. However, robot-assisted haptic rendering of IADLs is extremely challenging due to their complex dynamic properties and has been rarely reported. Considering the broad impedance range characteristics from free motion to hard contact, nail hammering (NH) is chosen as a representative IADL task. This paper presents our attempts to render the NH task via a customized robot. The core technologies consist of two aspects: 1) a robot-assisted haptic modeling technique with guaranteed accuracy and computation cost (by combining practical measurement data and experience-dependent analytical functions); 2) a robot-assisted haptic rendering technique involving a haptic robot with broad impedance range and sufficient force feedback (via a low gear ratio cable transmission and redundant actuation parallel mechanism) and closed-loop impedance control with guaranteed passivity and stability. Human experiments demonstrate an accurate NH task rendering that all Pearson correlation coefficients between real and virtual tasks are larger than 0.89. The modeling sensitivity analysis showed that stiffness parameter has the greatest effect on the realism of haptic rendering, with an effect size of 0.94. This study represents an important step towards comprehensive robot-assisted task-oriented therapy with haptic feedback.Note to Practitioners—The motivation of this work is to explore the techniques of robot-assisted haptic rendering of IADL tasks. On the one hand, current task modeling approaches are hard to balance accuracy and computational efficiency. On the other hand, existing haptic platforms have difficulty in meeting the broad impedance range and large force output requirements. In this work, we firstly developed a customized haptic robot via redundant actuation (enabling high robotic stiffness and force output) and low gear ratio cable transmission (enabling low friction and high back-drivability). We then built the nail hammering (NH) task model by combining practical measurement data (for accuracy) and experience-dependent analytical functions (for computational efficiency). Finally, we achieved the haptic rendering of the NH task using closed-loop impedance control with passivity and stability analysis. The proposed robot-assisted haptic modeling and rendering techniques can be extended to the haptic display of other types of IADL tasks.

While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.