Human-Robot Cooperative Strength Training Based on Robust Admittance Control Strategy.

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human-robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human-robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism.

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots.

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients' participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients' participation in training. By establishing the static equation of the mechanical leg, the man-machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers' motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm.

Recent Progress in Modification of Polyphenylene Oxide for Application in High-Frequency Communication.

With the rapid development of highly integrated electronic devices and high-frequency microwave communication technology, the parasitic resistance-capacitance (RC) delay and propagation loss severely restrict the development of a high-frequency communication system. Benefiting from its low dielectric constants (Dk) and low dielectric loss factor (Df), polyphenylene oxide (PPO) has attracted widespread attention for its application in the dielectric layers of integrated circuits. However, PPO suffers from a very high melting viscosity, a larger coefficient of thermal expansion than copper wire and poor solvent resistance. Recently, many efforts have focused on the modification of PPO by various means for communication applications. However, review articles focusing on PPO are unexpectedly limited. In this article, the research progress concerning PPO materials in view of the modification of PPO has been summarized. The following aspects are covered: polymerization and design of special chemical structure, low molecular weight PPO and blending with thermosetting resin, hyperbranched PPO, thermosetting PPO and incorporating with fillers. In addition, the advantages and disadvantages of various types of modification methods and their applications are compared, and the possible future development directions are also proposed. It is believed that this review will arouse the interest of the electronics industry because of the detailed summary of the cutting-edge modification technology for PPO.

A novel self-healing power cable insulating material based on host-guest interactions.

The insulating materials used in power cables are susceptible to damage and cracks during installation and operation. To solve this problem, we have prepared a self-healing material PVP/p(HEMA-co-BA), which is synthesized by radical polymerization using HEMA, BA, PVP and a host-guest assembly. The host-guest assembly is constructed through interactions between host and guest molecules (CD-Al2O3 NPs act as the host, and HEMA-Ad acts as the guest). The characterization results of the materials show that there are two kinds of supramolecular interactions, namely, the host-guest interaction and the hydrogen bonding. The material possesses good thermal stability (heat-resisting temperature can reach 200 °C) and good electrical performance. The storage modulus of the material can be increased up to 432 MPa using a cross-linking agent at 20 °C. Furthermore, the material exhibits self-healing property, and it can self-heal several times; its self-healing efficiency is relative to the dosage of the cross-linking agent.