Research on the lower limb exoskeleton for rehabilitation have developed rapidly to meet the need of the aging population. The rehabilitation exoskeleton system is a wearable man–machine integrated mechanical device. In recent years, the vigorous development of exoskeletal technology has brought new ideas to the rehabilitation and medical treatment of patients with motion dysfunction, which is expected to help such people complete their daily physiological activities or even reshape their motion function. The rehabilitation exoskeletons conduct assistance based on detecting intention, control algorithm, and high-performance actuators. In this paper, we review rehabilitation exoskeletons from the aspects of the overall design, driving unit, intention perception, compliant control, and efficiency validation. We discussed the complexity and coupling of the man–machine integration system, and we hope to provide a guideline when designing a rehabilitation exoskeleton system for the lower limbs of elderly and disabled patients.

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.

The arch of the human foot plays a significant role in bearing weight and keeping gait balance. Previous studies mainly focus on the foot arch stiffness at the static or quasi-dynamic state of a particular foot shape. The variation of the linear arch stiffness across the entire walking gait has rarely been reported. This work presents a phase division-based multi-segment foot model that considers plantar aponeurosis’s tension force for calculating the dynamics of the medial longitudinal arch. Kinematics and ground reaction forces of 10 healthy young adults during walking are recorded and analyzed. The stiffness changes of the foot arch throughout the stance phase are calculated. The experimental results show that the foot arch experiences a stiff-compliant-stiff-compliant transition during a single stance phase, including an extremely low stiffness during the plantar contact phase. By comparing the foot arch stiffness results with those from previous studies, the accuracy of the proposed model is indirectly validated. This study presents a new approach to explore the variation of the linear stiffness of the foot arch across the entire stance phase during walking. The proposed multi-segment foot model provides a new method for solving foot dynamics that can be used for wearable sensing and assistive design and applications.