## 1 Introduction
### Related Work
The ultimate goal was to represent three states of arm muscles; (i) arm in rest, (ii) lifted arm without load and (iii) lifted arm with load. The authors in [PERSON] et al. [7] observed that detecting human hand gestures prove useful for classification purposes. Specific patterns are extracted from EMG signals for each human arm movement and, later, a set of features were extracted those patterns and later feed to a neural network for training and classification. Those features are: mean absolute value (MAV), root mean square (RMS), Variance (VAR), Standard deviation (STD), Mean frequency (MF), Zero crossing (ZC) and Slope signal change (SSC). The ANN, in turn, classifies signals from the gestures. The authors noticed that the efficiency of classification can be improved if more signals are feed to NN. The average for the best overall training is reported in this word was 83.5%. The authors in [PERSON] et al. [8] have also introduced a hand-gesture recognition technique based on temporal EMG signals. As in [PERSON] et al. [7], EMG pattern signatures were extracted from EMG signals, later using NN, EMG signals were classified on those extracted features. The authors have used a back-propagation (BP) NN with Levenberg-Marquardt training to detect arm gestures. The features used in this research include the features used in [PERSON] et al. [7]. In addition, the authors used added the wavelet length (WL) feature. In this work, EMG signals were classified with an average success rate of 88.4%. [PERSON] et al. [8], proposed using technique of sEMG based in independent component analysis (ICA). The technique used blind source separation (BSS) methods, assumptions used may affect the precision of the results, for example the BSS model assumes that the mixing process is linear but in real, it can be not. In [8] and [PERSON] et al. [9], the ICA was used with many assumptions for example: the sources are independent and stationary, etc. In our current work, most of these assumptions were replaced with case facts. In [PERSON] et al. [8], the analysis was performed for only three-finger flexion action even though the results indicated have motivated our work for the existence of clear separation of patterns of sEMG. [PERSON] et al. [10] proofed the association between the sEMG and force of muscle contraction is accepted. [PERSON] et al. [11], conducted a study of the effects of a combined endurance training on fatigue kinetics during and immediately after a fatigue test. Recommending further study and methods, [PERSON] et al. [12] studied twelve young men performing three exercises with full measurements for the voluntary concentric or eccentric contractions until a reduction in maximal voluntary isometric contraction (MVC) torque of at least 40% MVC was achieved immediately post-exercise. In previous research [11; 12; 13], the specialists, physiology authors conducted an interesting studies in the effects of cycling training on neuromuscular fatigue and its peripheral, there work come up with outcomes which can be used in future extended work to clarify the neuromuscular function using enhanced neural network infrastructure which is used in this article, the input and the output of their work can be used as a training data for specific ANN training for further cases included in their work. Different applications could benefit from the results of this research by applying artificial neural network as in [14; 15].