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Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

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For heavy-duty bearings with low-viscosity lubricant, asperities may contact near the minimum nominal film thickness, while turbulence may develop in the area with large film thickness. When bearings encounter transient impact or unsteady loads, the influence of turbulence and surface roughness is relatively complex. To investigate transient lubrication and dynamic characteristics of mixed-lubricated bearings with turbulent flow, a transient mixed-lubrication model considering turbulence is proposed in this paper. A transient generalized average Reynolds equation is derived based on the Ng-Pan turbulence model. The transient journal center positions are obtained by solving the journal's dynamic equation. The numerical procedure is established. Based on the proposed model, the effect of turbulence, surface roughness, and transient impact load direction as well as the magnitude on transient lubrication and dynamic characteristics of mixed-lubrication bearings is analyzed. The results show that turbulence increases the transient minimum nominal film thickness and may decrease the transient friction force in the mixed-lubrication regime. Surface roughness modifies the dynamic trajectory of the journal center and increases both the transient minimum nominal film thickness and the friction force in the mixed-lubrication regime. The impact load direction significantly affects transient characteristics of the bearing. An increase in the load-deflection angle destabilizes the bearing operation state.