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Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.

In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations. Taking into consideration the modeling case at par with uncertain nonlinear systems, the implementation of neural network technique is contributed in the complex way of dealing the appropriate coefficients of the fuzzy equations. We use the neural network method to approximate Z-number coefficients of the fuzzyequations.

Students’ textual feedback holds useful information about their learning experience, it can include information about teaching methods, assessment design, facilities, and other aspects of teaching. This can form a key point for educators and decision makers to help them in advancing their systems. In this paper, we proposed a data mining framework for analysing end of unit general textual feedback using four machine learning algorithms, support vector machines, decision tree, random forest, and naive bays. We filtered the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data subset, We ran the above algorithms on the whole data set, and on the new data subsets. We also, adopted a semi automatic approach to check the classification accuracy of assessment related instances under the whole data set model. We found that the accuracy of general feedback data set models were higher than the accuracy of the assessment related models and nearly the same value of the non- assessment related modeles. The accuracy of assessment related models were approximated to the accuracy of the assessment related instances under the full data set models.