## 3 Results
### Effects of Performance with Variation in Model Factors and Parameters  
Variation of performance values across levels of classification algorithms (SVM, RF, XGB) and model tuning techniques (Baseline, SMOTE, GRID) are shown in Figure 4. The highest performance was observed when using the XGB algorithm and when using the Grid search tuning technique. Among the classification algorithms, SVM demonstrated the lowest performance values. Performance improved after using SMOTE to balance the number of datapoints between groups of different fatigue levels, and even higher performance was achieved after performing hyperparameter tuning using Grid search. Performance of predicting the fatigue level across the six different grouping methods is depicted in Figure 5. The highest performance was seen in the second grouping method for the binary classification of back fatigue (FB2G2) as well as leg fatigue (FI2G2) levels. On the other hand, the FB2G3 grouping method demonstrated the lowest performance. Table 4 shows the variation in model performance with a reduction in sensors for a model utilizing EMG and Kinematics measures with the XGB algorithm (GRID). Using both sensors on the low back resulted in an accuracy of 95%, which dropped if either only LES or RES was used. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fatigue** & **Grouping** & **Measure** & **Sensors** & **Location** & **C** & **A** & **P** & **S** & **R** & **F1** & **GI** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Leg \\ (Binary) \\ \end{tabular} } & FL2G2 & EMG, Kinematics & 2 & LES, LBF & XGB & 0.95 & 0.97 & 0.83 & 0.97 & 0.97 & 0.04 \\ \cline{2-11}  & FL2G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & XGB & 0.94 & 0.96 & 0.78 & 0.97 & 0.97 & 0.05 \\ \cline{2-11}  & FL2G2 & EMG & 4 & LES, LBF, RES, RBF & RF & 0.93 & 0.95 & 0.72 & 0.96 & 0.96 & 0.06 \\ \cline{2-11}  & FL2G2 & EMG, Kinematics & 2 & LBF, RBF & RF & 0.93 & 0.95 & 0.72 & 0.96 & 0.96 & 0.06 \\ \cline{2-11}  & FL2G2 & EMG, Kinematics & 1 & RES & RF & 0.93 & 0.96 & 0.78 & 0.95 & 0.96 & 0.06 \\ \cline{2-11}  & FL2G2 & Kinematics & 1 & LBF & RF & 0.88 & 0.94 & 0.67 & 0.91 & 0.93 & 0.11 \\ \hline \multirow{6}{*}{\begin{tabular}{c} Leg \\ (Tertiary) \\ \end{tabular} } & FL3G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & XGB & 0.79 & 0.78 & 0.79 & 0.79 & 0.78 & 0.31 \\ \cline{2-11}  & FL3G2 & EMG & 4 & LES, LBF, RES, RBF & RF & 0.77 & 0.76 & 0.77 & 0.77 & 0.76 & 0.33 \\ \cline{2-11}  & FL3G2 & Kinematics & 4 & LES, LBF, RES, RBF & XGB & 0.74 & 0.74 & 0.74 & 0.74 & 0.74 & 0.37 \\ \cline{2-11}  & FL3G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & RF & 0.74 & 0.73 & 0.74 & 0.74 & 0.73 & 0.37 \\ \cline{2-11}  & FL3G1 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & RF & 0.74 & 0.73 & 0.74 & 0.74 & 0.73 & 0.38 \\ \cline{2-11}  & FL3G1 & EMG & 4 & LES, LBF, RES, RBF & RF & 0.72 & 0.72 & 0.72 & 0.72 & 0.71 & 0.40 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Back \\ (Binary) \\ \end{tabular} } & FB2G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & XGB & 0.88 & 0.89 & 0.62 & 0.97 & 0.93 & 0.11 \\ \cline{2-11}  & FB2G2 & EMG & 4 & LES, LBF, RES, RBF & XGB & 0.86 & 0.90 & 0.69 & 0.91 & 0.91 & 0.13 \\ \cline{2-11}  & FB2G2 & Kinematics & 4 & LES, LBF, RES, RBF & RF & 0.86 & 0.89 & 0.62 & 0.93 & 0.91 & 0.13 \\ \cline{2-11}  & FB2G2 & EMG, Kinematics & 1 & RES & XGB & 0.85 & 0.89 & 0.66 & 0.91 & 0.90 & 0.14 \\ \cline{2-11}  & FB2G2 & Kinematics & 1 & RES & XGB & 0.82 & 0.89 & 0.66 & 0.87 & 0.88 & 0.17 \\ \cline{2-11}  & FB2G2 & Kinematics & 1 & LES & XGB & 0.82 & 0.89 & 0.66 & 0.87 & 0.88 & 0.17 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Back \\ (Tertiary) \\ \end{tabular} } & FB3G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & XGB & 0.67 & 0.67 & 0.67 & 0.67 & 0.67 & 0.47 \\ \cline{2-11}  & FB3G3 & EMG & 4 & LES, LBF, RES, RBF & XGB & 0.67 & 0.66 & 0.67 & 0.67 & 0.65 & 0.48 \\ \cline{1-1} \cline{2-11}  & FB3G3 & EMG & 4 & LES, LBF, RES, RBF & XGB & 0.67 & 0.65 & 0.67 & 0.67 & 0.66 & 0.48 \\ \hline \hline \end{tabular}
\end{table}