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Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-optimized numerical method is presented for approximation of HIV virus progression model in the human body. The model is composed of coupled nonlinear system of differential equations (DEs) containing healthy and infected T-Cells and HIV free virus particles. The coupled system is transformed into feedforward artificial neural network (ANN) with Mexican hat wavelet function in the hidden layers. Two meta-heuristic algorithms based on chaotic particle swarm optimization (CPSO) and its hybrid version with local search technique are exploited to tune the parameters of ANN in an unsupervised manner of error function. A comprehensive testbed is established to observe the virus growth per day with performance metric containing fitness value, computational time complexity and convergence. The proposed solutions are compared with state of art Runge–Kutta method and Legendre Wavelet Collocation Method (LWCM). The core advantages of the proposed scheme are getting the solution on continuous grid, consistent convergence, simplicity in implementation and handling strong nonlinearity effectively. [ABSTRACT FROM AUTHOR]