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.

The development of assistive lower-limb exoskeletons gains prominence for human load-carrying augmentation. Hydraulic transmission has attractive hydrostatic features and lower inertia at the end of human limbs. However, few hydraulic lower-limb exoskeletons were developed with low energy consumption and light weight. In this article, we introduce HyExo, a quasi-passive hydraulic exoskeleton that is built on a lightweight rotary cage valve (RCV) block with a fast response and low energy consumption of 1.55 W. Based on the RCV block, we propose an optimization-based regulator for joint energy distribution to harvest and release the hydraulic energy among joints during the stance phase. The interaction force model and control of the novel nonanthropomorphic structure are presented and evaluated. The load-supporting effect was investigated and validated through human subject experiments. The results show that with an assisting fluid pressure of 2.5 MPa, HyExo can transfer a mean force of 237 N to the ground. Meanwhile, the impact of wearing HyExo on gait is analyzed. The metabolic expenditure test shows that HyExo can slow the increasing rate in metabolic cost as load increases. Compared with a regular backpack, walking with HyExo to carry 30 kg of weight reduces wearers' metabolic energy expenditure by 7.8%.

Unpowered exoskeletons (UEs) have attracted extensive research attentions for their portability, handleability and simplicity. However, designing exoskeletons without any active actuators is a great challenge because essential criteria such as performance enhancement, low impedance, and comfort must be met. This paper reviews the UEs researches from three aspects including modeling for understanding gait pattern, mechanical designs, and validation methods, which are main considerations of designing UEs. This review is based on the reported works in the past two decades and underlines some practical opportunities and challenges. Statistical data analysis and critical reviews are stated to understand human gait patterns, designing of UEs, and ergonomic validation. This review is trying to inspire a common understanding about passive exoskeletons for lower extremity and promote discussion among researchers, developers, or robotic practitioners.

Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated.

Pressure ulcers (PU) are one of the most frequent hazards of long-term bedridden patients. With the continuous increase of aging, the number of long-term bedridden disabled and semi-disabled elderly people is increasing. At the same time, there is a serious shortage of professional pressure ulcer nursing staff. There is also a lack of flexible turning equipment for PU prevention. The research in the field of pressure ulcer prevention at home and abroad is carried out steadily, and the equipment for turning over by pneumatic or mechanical drive is developed. However, these devices often have insurmountable defects, such as complex structure, cost constraints, difficult control, weak body feeling, and so on. Under these circumstances, a set of pneumatic turnover mattresses based on clinical nursing methods have been developed. The mattress is divided into a turnover area and two support areas. The turnover airbag is linked with the support airbag to improve the patient’s comfort when passively turning over. The turnover amplitude and interval can be adjusted to provide a personalized turnover experience for bedridden patients. To improve the safety of the turning mattress during automatic turning, we also add a temperature sensor based on the principle of infrared reflection to monitor the status of bedridden patients, which can realize real-time temperature measurement, monitoring of getting out of bed and monitoring of the turning process.

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.