## 3 Results
### Processing the SEMG Signal  
After collecting the raw SEMG signals with different loads as shown in Figures 7-9. The amplitude of the SEMG signals for Subject C with a 3 kg load has been selected as reference amplitude of the SEMG signals in this experiment as shown in Figure 10. After converting the SEMG signal to the frequency domain using the Fast Fourier transform (FFT) as shown in Figure 11, one can observe the power of the SEMG signal below 10 Hz. After determining the cutoff frequency, the SEMG signal is passed through a fifth-order Butterworth low-pass filter with a cut off frequency of 10 Hz and the smooth signal is shown in Figure 12.  
Figure 11: FFT the SEMG signal for 3 Kg.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Subject** & **Load** & **IEMG** & **MAV** & **VAR** & **RMS** & **STD** \\ \hline \multirow{3}{*}{**A**} & 3 & 1,093,437 & 156.183 & 12746.67 & 192.7121 & 112.9011 \\  & 5 & 1,760,043 & 251.3988 & 66570.16 & 360.2249 & 255.308 \\  & 7 & 1,822,112 & 260.2645 & 65182.16 & 364.5689 & 258.0119 \\ \hline \multirow{3}{*}{**B**} & 3 & 1,135,594 & 162.2045 & 14573.65 & 202.1927 & 120.7214 \\  & 5 & 1,326,611 & 189.4888 & 25931.63 & 248.6643 & 161.033 \\  & 7 & 1,792,779 & 256.0747 & 47465.66 & 336.204 & 217.8661 \\ \hline \multirow{3}{*}{**C**} & 3 & 1,048,218 & 149.724 & 10311.8 & 180.9078 & 101.547 \\  & 5 & 2,199,400 & 314.1551 & 51050.46 & 386.9581 & 225.9435 \\ \cline{1-1}  & 7 & 2,795,416 & 399.2881 & 63493.9 & 472.1396 & 251.9799 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average values of five time domain features with three loads.  
Figure 10: Original SEMG signal for 3 kg load.  
This research has chosen Subject C to create the model nerve signal for the bicep muscle. As can be observed in Figure 14, the fit linear model shows the relationship between amplitude SEMG signal and loads for Subject C of five time domain features (IEMG, MAV, VAR, RMS and STD) by using regression analysis with R-squared value. Based on the regression analysis, the better-fit model is the one with a higher R-squared or has a R-squared close to 1.00. Based on the result as shown in Table 3, IEMG and MAV have the same R-squared value that is equal to 0.9674. This result means two approaches to model the relationship between amplitude SEMG signal and load. Table 3; shows all equations as fit linear model for five features (where x is the load (i.e., 3, 5 or 7 kg)). This research had chosen MAV as the reference feature to estimate the bicep muscle force that will be used in building modeling nerve signal for bicep muscle.  
The bicep muscle force can be estimated by using Equation (7): [5; 6].  
\[\text{y}=62.391\text{x}-24.233 \tag{7}\]  
By comparing the y with \(Amp\) and x with \(L\), \(Amp=62.391L-24.233\) (8)  
\[L=0.016\;Amp+0.388 \tag{9}\]  
where \(L\) is the load and \(Amp\) is the amplitude of the SEMG signal, this means when the average amplitude of the SEMG signal \(Amp\) is 163; the value of the load (\(L\)) is \(\sim 3\) kg  
By comparing \(F\) with \(L\),  
\[F_{load}=0.016\;Amp\;+\;0.388 \tag{10}\]  
In Equation (10), \(Amp\) is the amplitude of the SEMG signal and \(F\) is the average bicep muscle force. Note that this force should be multiplied with the gravity coefficient. This means that when \(Amp\) is 163, then \(F\) is 29.3608 Newton.