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.

Upper limb rehabilitation exoskeleton can be a good substitute for professional therapists in rehabilitation training for stroke patients, but few devices can accurately obtain the upper limb rehabilitation movements of therapists or patients on the healthy side to guide the rehabilitation exoskeleton in passive rehabilitation of patients. In this paper, a 10-DOF exoskeletal master device is proposed, which includes all the degrees of freedom of upper limb, and is improved from two aspects of kinematic analysis and structural design. By studying the motion of the whole upper limb, especially the shoulder joint, the complete upper limb motion chain is proposed in this paper, and the feasibility is verified by kinematics analysis. Then the structure design is carried out and introduced in detail. Finally, genetic algorithm was used to optimize the structural parameters of pHRI, which provided a reference for Physical human–robot-interface (pHRI) transplantation to different exoskeleton devices.