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.

With the increasing aging population in China and the trend of younger onset of elderly diseases, more middle-aged and elderly people are suffering from stroke and related complications. Considering the increasing needs in rehabilitation service, exoskeletons for rehabilitation training have become a urgent solution. This article starts by analyzing the finger skeleton and designs a dorsal-style hand exoskeleton which assists patients in individual rehabilitation training for each finger, enabling them to perform simple grasping activities in daily life. Moreover, by using a single active driver, the exoskeleton can systematically drive the two adjacent joints of a patient's finger, ensuring that their movement aligns with the typical grasping patterns of a healthy individual. This design is more beneficial for the patient's rehabilitation. Finally, a comparison is made between the range of motion of the finger in a healthy state and when wearing the exoskeleton to ensure the safety of the exoskeleton.