In this paper, we propose a regression model to establish a relationship between surface electromyography (sEMG) and knee joint angles. In this model, the correlation dimension of wavelet coefficient (WCCD) and an Elman network are developed for the model estimation. In our experiment, the sEMG signals were recorded from five muscles concerned with knee joint motion, and knee joint angles were simultaneously recorded by a Codamotion system. First, we used a feature extraction method based on WCCD to extract optimal feature vectors from multichannel sEMG signals. Then, the Elman network was used to map the optimal sEMG features to the knee joint angles. The results show that the features extracted from the multichannel sEMG signals using the WCCD method proposed in this paper outperform the time-domain and frequency-domain methods. Our method is expected to be applied to intelligent prosthetics, exoskeleton robots, and medical rehabilitation robots.

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the ${\boldsymbol{\beta}}$ 1, ${\boldsymbol{\beta}}$ 2, and $\ {\boldsymbol{\gamma}}$ 2 bands, among which, it was highest in the ${\boldsymbol{\beta}}$ 1 band, and reached its maximum at the 22–30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the ${\boldsymbol{\beta}}$ 1 and ${\boldsymbol{\beta}}$ 2 bands and the stroke-unaffected side in the ${\boldsymbol{\beta}}$ 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of ${\boldsymbol{\gamma}}$ 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction.

Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.

In rough set theory, attribute reduction is a basic issue, which aims to hold the discernibility of the attribute set. To obtain all of the reducts of an information system or a decision table, researchers have introduced many discernibility matrices based reduction methods. However, the reducts in the sense of positive region can only be obtained by using the existing discernibility matrices. In this paper, we introduce two discernibility matrices in the sense of entropies (Shannon’s entropy and complement entropy). By means of the two discernibility matrices, we can achieve all of the reducts in the sense of Shannon’s entropy and all of the reducts in the sense of complement entropy, respectively. Furthermore, we discover the relationships among the reducts in the sense of preserving positive region, Shannon’s entropy and complement entorpy. The experimental studies show that by the proposed decision-relative discernibility matrices based reduction methods, all the reducts of a decision table in sense of entropies can be obtained.

Enteroviruses are found in most environments and cause several diseases in humans. Loop-mediated isothermal amplification (LAMP) was adapted and evaluated for the rapid detection of enteroviruses. Based on the highly conserved 5′ untranslated region (5′-UTR) of the human enteroviruses (HEVs), particularly human enterovirus A (HEV-A) and HEV-B, a set of universal primers was designed. The LAMP amplification was carried out under isothermal conditions at 61 °C, depending on the template concentration results were obtained within 45–90 min. The detection limits were found to be 10¹ copies of cloned enterovirus 71 fragments, more sensitive than conventional PCR. Nine water samples collected from drinking water sources during three seasons and 19 stool specimens collected from HFMD patients were analyzed. By using the LAMP assay, the majority of samples was tested positive, 9/9 (100 %) and 18/19 (94.7 %), respectively. LAMP is a practical method for the rapid detection of enteroviruses in environmental and clinical samples.

The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.