## 1 Introduction
### Related Work  
In the last few years, several works involving modelling the EMG signal have been investigated in areas such as motion recognition and force estimation. [PERSON] et al. [2] has introduced a model that illustrates the correlation between muscle EMG signal and torque using ANN techniques. For that, EMG signals from biceps muscles are collected and later used to estimate the elbow joint torque temporal values. As a preprocessing step, the authors used a low-pass filter with a cutoff frequency of 10 Hz. Further, a DC offset is subtracted from the collected signals before they are rectified to obtain its absolute value. The transformed signals are then smoothed and normalized through having them pass through a fifth-order Butterworth-type low-pass filter. Smoothed signals are then modeled using a two-layer feed-forward neural network. In this regard, the ANN is trained using a back propagation neural network (BPNN). The performance of the introduced procedure is determined through calculating the mean square error (MSE) and regression (\(R^{2}\)). The proposed model demonstrated an MSE of \(1.7638e^{-11}\) at epoch 1000 and average regression of 1.000 [2]. [PERSON] et al. [3] presented a new mathematical equation to model the elbow joint torque from the electromyography (EMG) signal. Again, preprocessing is done to (i) remove the DC offset, (ii) remove 60 Hz of noise and (iii) estimate the smoothed EMG signal amplitude. The detected amplitude is later mapped to joint-torque using a novel nonlinear interpolation equation. An experimental set of 10 subjects was used during experiments, each of which has performed variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC). The experimental results obtained from this study are observed to be correlated to the true values of the torque with the average correlation and MSE for different experiments are 0.9997 and 0.047 Newton meter (Nm); respectively. In [PERSON] et al. [4], the authors introduced a procedure model muscle activation from EMG signals, again using neural network techniques. The authors predict muscle activations from temporal EMG signals without constructing complex mathematical models to model muscle activation dynamics. The NN model of muscle activation applied in this work is of four layers and utilizes an improved back-propagation for the training step. Joint torque was calculated from EMG signals collected from ten flexor and extensor muscles. A musculoskeletal geometry model is built to obtain moment arms, from which joint moments were estimated comparatively evaluated against measured values. Study results successfully represent the relationship between EMG signals and joint moments. In [PERSON] et al. [5], the authors have extracted several features of temporal EMG signals during hand-lifting of several loads. The authors suggested that interpreting EMG signals in time domain through feature extraction is better than studying those signals in frequency domain. In that regard, a set of four different features have been extracted. Those are: IEMG, MAV, VAR and RMS. The average muscle force condition is calculated based on the EMG signal peak amplitude and the full-wave rectification method. The \(R^{2}\) value is used to depict the correlation between the EMG signal amplitude with the muscle load. The extracted IEMG feature is chosen as a reference feature for estimating of muscle force with R-squared value of 0.997. In [PERSON] et al. [6], the authors utilized the MAV method to estimate bicep force based on a collected set of surface electromyography (SEMG) signals. For that, different loads have been applied on the targeted muscle. Those are: zero, 2 kg, 3 kg, 4 kg and 5 kg. Noise in SEMG signals was reduced by having them pass through a Butterworth band-pass filter (BPF) with low and high cutoff frequencies of 10 Hz and 40 Hz, respectively. The authors used the mean absolute value (MAV) method to estimate the average muscle's force based on the SEMG voltage amplitude.