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.

Multifarious flexure-based displacement amplifiers have been proposed and studied in the past decades, showing importance in many industrial fields, such as bioengineering, optical instruments, and semiconductor technology. Displacement amplifiers provide precise motion and large stroke through a simple and low-cost way compared with other piezoelectric actuators. Those merits have opened a door for new and advanced micro devices with unprecedented performance. This paper aims to proffer a comprehensive review on the design, modeling, characteristics, and applications of flexure-based displacement amplifiers, following by pointing out the inherent drawbacks in this research area such as amplification ratio limit, parasitic motion, low lateral stiffness, low natural frequency, and discussing existing solutions and some potential research directions in those topics. Finally, a summary is concluded and the future development perspectives of the displacement amplifiers are discussed. This review contributes to giving a comprehensive understanding of the displacement amplifier, which provides guidance on designing new displacement amplifiers for improving their mechanical output performance. It is also expected to be instrumental for related researchers to understand displacement amplifiers, and to successfully select and design for specific applications.

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system, which conducts natural and close cooperation with the human by recognizing human locomotion timely. Requiring subject-specific training is the main challenge of the existing approaches, and most methods have the problem of insufficient recognition. This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition (LMR) method based on GA-CNN for a lower limb exoskeleton system. The LMR method is a combination of Convolutional Neural Networks (CNN) and Genetic Algorithm (GA)-based multi-sensor information selection. To improve network performance, the hyper-parameters are optimized by Bayesian optimization. An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method. Twelve locomotion modes, which composed an integral locomotion system for the daily application of the exoskeleton, can be recognized by the proposed method. According to a series of experiments, the recognizer shows strong comprehensive abilities including high accuracy, low delay, and sufficient adaption to different subjects.