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.

Selecting an appropriate number of surface electromyography (EMG) channels with desired classification performance and determining the optimal placement of EMG electrodes would be necessary and important in practical myoelectric control. In previous studies, several methods such as sequential forward selection (SFS) and Fisher-Markov selector (FMS) have been used to select the appropriate number of EMG channels for a control system. These exiting methods are dependent on either EMG features and/or classification algorithms, which means that when using different channel features or classification algorithm, the selected channels would be changed. In this study, a new method named multi-class common spatial pattern (MCCSP) was proposed for EMG selection in EMG pattern-recognition-based movement classification. Since MCCSP is independent on specific EMG features and classification algorithms, it would be more convenient for channel selection in developing an EMG control system than the exiting methods. The performance of the proposed MCCSP method in selecting some optimal EMG channels (designated as a subset) was assessed with high-density EMG recordings from twelve mildly-impaired traumatic brain injury (TBI) patients. With the MCCSP method, a subset of EMG channels was selected and then used for motion classification with pattern recognition technique. In order to justify the performance of the MCCSP method against different electrode configurations, features and classification algorithms, two electrode configurations (unipolar and bipolar) as well as two EMG feature sets and two types of pattern recognition classifiers were considered in the study, respectively. And the performance of the proposed MCCSP method was compared with that of two exiting channel selection methods (SFS and FMS) in EMG control system. The results showed that in comparison with the previously used SFS and FMS methods, the newly proposed MCCSP method had better motion classification performance. Moreover, a fixed combination of the selected EMG channels was obtained when using MCCSP. The proposed MCCSP method would be a practicable means in channel selection and would facilitate the design of practical myoelectric control systems in the active rehabilitation of mildly-impaired TBI patients and in other rehabilitation applications such as the multifunctional myoelectric prostheses for limb amputees.

As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM) can not avoid the conventional challenges, such as how to select the appropriate number of mixture components based on the observed data. Towards easing these issues, we propose a variational inference framework for learning IDMM which has been proved to be an efficient tool for modeling vectors with positive elements. Compared with the conventional Expectation maximization (EM) algorithm commonly used for learning FMM, the proposed approach prevents over-fitting well. Furthermore, it is able to do automatic determination of the number of mixture components and parameters estimation, simultaneously. Experimental results on both synthetic and real data of object detection confirm significant improvements on flexibility and efficiency being achieved.

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.

In this paper, we designed a hemiplegic upper limb rehabilitation training system, which allowed both single degree of freedom and composite degrees of freedom for the training of shoulder and elbow. The system contained an upper limb rehabilitation robot, a pattern recognition system and a motion control system. Firstly, we designed a novel upper limb rehabilitation robot with three degrees of freedom, with the motor and reducer innovatively placed centrally in the shoulder of the mechanical limb arm. The movement is more stable because the inertia of each joint movement is reduced. The design makes simultaneously training both the left and right arm possible. In the control system design, the movement coupling problem is solved through the inverse operation of the target action. Lastly, to further enrich information of the single feature vector, a method integrating the AR model coefficients and wavelet coefficients was proposed. A method combining the Particle Swarm Optimization Algorithm and Levenberg-Marquardt (LM) was used to optimize the BP networks, addressing the problems associated with lower convergence speed and local minimum of standard BP networks. The experiments showed that the convergence speed of the network and the recognition rate of the target action were effectively improved, which demonstrated the effectiveness of the training system.