*Article* **Hand Exoskeleton Design and Human–Machine Interaction Strategies for Rehabilitation**

**Kang Xia 1,2,\*, Xianglei Chen 1, \*, Xuedong Chang 1 , Chongshuai Liu 1 , Liwei Guo 3 , Xiaobin Xu 1 , Fangrui Lv 1 , Yimin Wang <sup>3</sup> , Han Sun <sup>3</sup> and Jianfang Zhou 1**


**Abstract:** 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 linkagedriven 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.

**Keywords:** hand exoskeleton design; motion simulation; rehabilitation; intention recognition; machine learning; deep learning
