## 3 Results
### Model Selection and Feature Importances  
For both the binary and tertiary grouping methods, a single model was chosen for predicting back and leg fatigue based on performance and accessibility (number of sensors), as depicted in Table 5. To explore model performance, confusion matrices (Figure 7) were plotted. Values in the left-right diagonal elements were better for binary models as compared to tertiary classification models, which were not as effective in distinguishing between low and high fatigue levels. In both binary models, the number of samples correctly classified as medium fatigue (True Positives) were much higher than the number of samples correctly identified as low fatigue. On the other hand, considering tertiary models, the number of samples correctly identified as low and high fatigue were much higher compared to medium fatigue class. In both models B and C, 16 and 17 samples belonged to high fatigue levels but were predicted to be a low level. Figure 6: Sensitivity analysis showing impact of variation in model parameters of max depth, learning rate, and subsample rate on the overall model accuracy. Accuracy improved with lower values of max depth and with higher values of learning rate and subsample rate (Note: model used for this example consisted of two sensors placed on left erector spinae (LES) and left biceps femoris (LBF) muscle groups with classification performed using XGBoost (XGB) classification algorithm based with Grid search (GRID) on kinematics measures. ). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Grouping** & \multirow{2}{*}{**Measure**} & \multirow{2}{*}{**Sensors**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**S**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**GI**} \\ \cline{1-1} \cline{5-12} A & FL2G2 & EMG, Kinematics & 2 & LES, LBF & 0.95 & 0.97 & 0.83 & 0.97 & 0.97 & 0.04 \\ \hline B & FL3G2 & Kinematics & 4 & LES, LBF, RES, RBF & 0.74 & 0.74 & 0.74 & 0.74 & 0.37 \\ \hline C & FB2G2 & Kinematics & 1 & RES & 0.82 & 0.89 & 0.66 & 0.87 & 0.88 & 0.17 \\ \hline D & FB3G2 & EMG, Kinematics & 4 & LES, LBF, RES, RBF & 0.67 & 0.67 & 0.67 & 0.67 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Selected models for each grouping method for back and leg (A), precision (P), specificity (S), sensitivity/recall (R), F1 score (F1), and G-index (GI) (Note: All models utilized XGB classification algorithm with Grid search (GRID) tuning technique.). For each of the selected models, we examined feature importance (Figures 8 and 9) to determine the most prominent features. Looking at feature importances, a lack of clear pattern was seen in the top 30 features for all models, and features from all portions contributed to overall performance. Overall, the normalized mean value of the norm of acceleration from the sensor placed on the RES during the sustained bending portion was one of the most prominent features in all models. Models A and D used features from both EMG and kinematics measures, while models B and C relied solely on kinematics measures. For models A, B, and C, features from EMG measures that represented changes in value ('n_') were among the most prominent features. Figure 7: Confusion matrices for (**a**,**c**) binary and (**b**,**d**) tertiary fatigue level grouping methods for the four selected models using XGBoost with Grid search (GRID) classification algorithm (Model A: FL2G2— EMG, Kinematics—LES, LBF; Model B: FL3G2— Kinematics—LES, LBF, RES, RBF; Model C: FL2G2— Kinematics—RES; Model D: FB3G2—EMG, Kinematics—LES, LBF, RES, RBF). Figure 8: Feature importances for binary and tertiary fatigue level grouping methods for the four selected models using XGBoost (GRID) classification algorithm (Model A: FL2G2—EMG, Kinematics—LES, LBF, Model B: FL3G2—Kinematics—LES, LBF, RES, RBF). (Note: format for features is in the form ‘feature name’, ‘sensor’, and ‘_portion’. Sensor represents location as s1: LES, s2: LBF, s3: RES, s4: RBF.