Surface Electromyography (sEMG) has been extensively used for gait analysis and robotic control. Since the low accuracy of intention recognition limits the development of exoskeleton robots, a dynamic adaptive neural network algorithm based on the multi-feature fusion of sEMG signals was proposed to realize the accurate identification of eight lower limb movements. Firstly, the original sEMG signals were collected from eighty volunteers through gait experiments. Secondly, the feature parameters of time domain, frequency domain, and sample entropy were extracted and merged. Then, the improved differential evolution (DE) algorithm was proposed to optimize the weight values of each feature for data fusion. Thirdly, the backpropagation neural network(BPNN) used in this study is the most common model for intention recognition. However, BPNN has the problems of low accuracy and slow convergence speed, this paper proposed a novel dynamic adaptive neural network model (GA-DANN). The novel model uses genetic algorithm (GA) to optimize the weights of the BPNN to accelerate convergence. The dynamic adaptive learning rate was introduced to overcome the difficulty of determining the learning rate. The experimental results show that the proposed algorithm with multi-feature fusion information could accurately distinguish eight lower limb motion intentions, and the averaged recognition accuracy reaches 94.89%, which is 10% higher than the traditional BPNN. The averaged recognition time is only 109.67ms. This research will provide technical support for the application of rehabilitation robots.

Epoxy and unsaturated polyester resins are the most used thermosetting polymers. They are commonly used in electronics, construction, marine, automotive and aircraft industries. Moreover, reinforcing both epoxy and unsaturated polyester resins with carbon or glass fibre in a fabric form has enabled them to be used in high-performance applications. However, their organic nature as any other polymeric materials made them highly flammable materials. Enhancing the flame retardancy performance of thermosetting polymers and their composites can be improved by the addition of flame-retardant materials, but this comes at the expense of their mechanical properties. In this regard, a comprehensive review on the recent research articles that studied the flame retardancy of epoxy resin, unsaturated polyester resin and their composites were covered. Flame retardancy performance of different flame retardant/polymer systems was evaluated in terms of Flame Retardancy index (FRI) that was calculated based on the data extracted from the cone calorimeter test. Furthermore, flame retardant selection charts that relate between the flame retardancy level with mechanical properties in the aspects of tensile and flexural strength were presented. This review paper is also dedicated to providing the reader with a brief overview on the combustion mechanism of polymeric materials, their flammability behaviour and the commonly used flammability testing techniques and the mechanism of action of flame retardants.