# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 3 Results
### Feature Importance Values  
Outcomes of performance evaluation showed high performance of RF and XGB classification algorithms. To identify specific contributions of features, feature importance values for Random Forest (RF) and XGBoost (XGB) algorithms were analyzed across measures of muscle activity (four sensors and single-sensor contributions), trunk motion, and whole-body stability. Looking at trunk motion as shown in Figure 3, acceleration parameters for the hip marker during the retraction portion emerged as the top contributors for RF (0.03-0.04), followed closely by parameters for the transition phase. Particularly, 'Max_normAccHIP1_R', 'Mean_normAccHIP1_R', 'var_normAccHIP1_R', which denotes the maximum value, mean value, and variance of the norm of acceleration of the hip marker \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Measures** & **Number of Features** & **Classification Algorithm** & \(A\) & \(R\) & \(S\) & \(P\) & _F1_ & _GI_ \\ \hline \multirow{6}{*}{Trunk Motion (UB, LB, Hip)} & \multirow{2}{*}{54} & SVM & 62.5 & 67.5 & 57.3 & - & - & - \\ \cline{3-8}  & & LR & 67.8 & 69.5 & 66.2 & - & - & - \\ \cline{3-8}  & & RF & 79.6 & 77.4 & 81.7 & 82.1 & 0.80 & 0.28 \\ \cline{3-8}  & & XGBoost & 80 & 79.7 & 80.3 & 80.5 & 0.79 & 0.28 \\ \hline \multirow{6}{*}{Muscle Activity (LES, RES, LBF, RBF)} & \multirow{2}{*}{48} & SVM & 74.4 & 69.2 & 79.9 & 77.6 & 0.72 & 0.38 \\ \cline{3-8}  & & LR & 76 & 75.4 & 77 & 76.6 & 0.75 & 0.34 \\ \cline{3-8}  & & RF & 94.5 & 93.6 & 95.5 & 93.7 & 93.6 & 0.09 \\ \cline{3-8}  & & XGBoost & 94.6 & 94.9 & 94.5 & 94.5 & 94.6 & 0.08 \\ \hline \multirow{6}{*}{Muscle Activity (LES, RES)} & \multirow{2}{*}{24} & SVM & 75.3 & 72.5 & 78.4 & 77.2 & 0.74 & 0.36 \\ \cline{3-8}  & & LR & 69.4 & 66.8 & 72.3 & 70.9 & 0.68 & 0.44 \\ \cline{3-8}  & & RF & 90.5 & 88.7 & 92.5 & 90.6 & 0.89 & 0.14 \\ \cline{3-8}  & & XGBoost & 91.3 & 91.5 & 91.2 & 91.2 & 0.91 & 0.12 \\ \hline \multirow{6}{*}{Muscle Activity (LES)} & \multirow{2}{*}{12} & SVM & 75.7 & 74.7 & 77.1 & 76.6 & 0.75 & 0.35 \\ \cline{3-8}  & & LR & 66.9 & 64.7 & 69.5 & 68.2 & 0.65 & 0.48 \\ \cline{3-8}  & & RF & 84.7 & 84.7 & 85 & 82.4 & 0.81 & 0.26 \\ \cline{3-8}  & & XGBoost & 85.1 & 85.4 & 84.9 & 85.2 & 85.2 & 0.21 \\ \hline \multirow{6}{*}{Muscle Activity (RES)} & \multirow{6}{*}{12} & SVM & 64.7 & 62.5 & 67.5 & 66.47 & 0.63 & 0.50 \\ \cline{3-8}  & & LR & 61.7 & 58.7 & 65.3 & 63.1 & 0.60 & 0.55 \\ \cline{3-8}  & & RF & 84.6 & 84.1 & 85.2 & 86.1 & 0.84 & 0.22 \\ \cline{3-8}  & & XGBoost & 87.1 & 86.7 & 87.7 & 87.6 & 0.87 & 0.18 \\ \hline \multirow{6}{*}{Muscle Activity—single sensor (avg(LES, RES))} & \multirow{6}{*}{12} & SVM & 64.7 & 68.6 & 72.3 & 71.5 & 0.69 & 0.42 \\ \cline{3-8}  & & LR & 64.3 & 61.7 & 67.4 & 65.6 & 0.62 & 0.51 \\ \cline{3-8}  & & RF & 84.6 & 84.4 & 85.1 & 84.2 & 0.82 & 0.24 \\ \cline{3-8}  & & XGBoost & 86.1 & 86 & 86.3 & 86.4 & 43.1 & 0.19 \\ \hline \multirow{6}{*}{Whole-Body Stability (Force Plate L/R foot)} & \multirow{6}{*}{33} & SVM & 61.1 & 49.2 & 73.1 & 64.4 & 0.55 & 0.61 \\ \cline{3-8}  & & LR & 52.9 & 59.5 & 46.7 & 53.2 & 0.56 & 0.62 \\ \cline{1-1} \cline{3-8}  & & RF & 92.9 & 91.9 & 94.1 & 94.5 & 0.94 & 0.09 \\ \cline{1-1} \cline{3-8}  & & XGBoost & 93.5 & 94.1 & 93.1 & 93.3 & 0.94 & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Outcomes showing the values of accuracy (_A_), sensitivity/recall (R), specificity (_S_), precision (_P_), F1 score (_F_1), and G-index (_GI_) with different classification algorithms categorized according to the type of measures of trunk motion (UB: upper back, LB: lower back, Hip: hip), muscle activity (LES: left erector spinae, RES: right erector spinae, LBF: left biceps femoris, RBF: right biceps femoris), and whole-body stability along with their respective features. during retraction portion showed the highest importance for RF. Meanwhile, XGB showed the 'Mean_normVelUB1_1' feature, denoting the average of the norm of velocity of the upper back marker during transition as the highest contributor.