## 2 Materials and Methods
### Performance Evaluation and Validation  
We used the train-test split method to assess the performance of machine learning models. All the data were divided into two sets: (a) training set, which was used to train the model for detecting the patterns across the datapoints, and (b) test set, which was used to evaluate the model. Performance metrics were calculated by making predictions on the unseen data (test set) using the model trained on training set. Metrics for assessing model performance included accuracy (A), sensitivity/recall (R), specificity (S), precision (P), F1-score (F1), and G-index (G) and were calculated in a similar manner as our prior study [56]. For calculating performance metrics, we first divided the data into two separate sets using the train-test split technique. Based on the predictions and the actual labels of the test data, we generated a confusion matrix for each model, with rows and columns corresponding to each class in our model. A confusion matrix represents four variables, as described below:  
* True Positive (TP): The number of instances correctly predicted as positive. * True Negative (TN): The number of instances correctly predicted as negative. * False Positive (FP): The number of instances incorrectly predicted as positive. * False Negative (FN): The number of instances incorrectly predicted as negative. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Level** & **Description** \\ \hline \multirow{4}{*}{Sensors} & s1 & Sensor on Left Exector Spinae (LES) \\ \cline{2-3}  & s2 & Sensor on Left Biceps Spinae (LBF) \\ \cline{2-3}  & s3 & Sensor on Right Exector Spinae (RES) \\ \cline{2-3}  & s4 & Sensor on Right Biceps Femoris (RBF) \\ \hline \multirow{4}{*}{Measures} & Muscle Activity & Surface Electromyography \\ \cline{2-3}  & Kinematics & Acceleration \\ \cline{2-3}  & Muscle Activity and Kinematics & Combination of Surface Electromyography and Acceleration \\ \hline \multirow{2}{*}{Fatigue Region} & FB & Fatigue in Back \\ \cline{2-3}  & FL & Fatigue in Legs \\ \hline \multirow{4}{*}{Binary (FX)} & FX2G1 & IF(Fatigue Level \(\leq\) 3,”L,”,”M”) \\ \cline{2-3}  & FX2G2 & IF(Fatigue Level \(\leq\) 2,”L,”,”M”) \\ \cline{2-3}  & FX2G3 & IF(Fatigue Level \(\leq\) 4,”L,”,”M”) \\ \cline{2-3}  & \multirow{4}{*}{Tertiary (FX)} & FX3G1 & IF(Fatigue Level \(\leq\) 2,”L,\",(IF(Fatigue Level \(\leq\) 4,”M”,”H”))) \\ \cline{2-3}  & & FX3G2 & IF(Fatigue Level \(\leq\) 1,”L,\",(IF(Fatigue Level \(\leq\) 3,”M”,”H”))) \\ \cline{2-3}  & \multirow{4}{*}{\
\begin{tabular}{c} Tertiary (FX) \\ \end{tabular} } & FX3G3 & IF(Fatigue Level \(\leq\) 3,”L,”,(IF(Fatigue Level \(\leq\) 5,”M”,”H”))) \\ \cline{2-3}  & & SVM & Support Vector Machine \\ \cline{2-3}  & RF & Random Forest \\ \cline{2-3}  & XGB & XGBost \\ \hline \multirow{4}{*}{Model-Tuning Techniques} & Baseline & - \\ \cline{2-3}  & SMOTE & Synthetic Minority Over-sampling Technique (SMOTE) \\ \cline{1-1} \cline{2-3}  & GRID &
\begin{tabular}{c} Finding best hyperparameters with cross validation \\ using grid search along with SMOTE \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of factors with their levels that were varied during performance evaluation for model selection. In this study, true positives (TP) represent activities correctly labeled as belonging to the fatigue level, while true negatives (TN) are activities correctly labeled as not belonging to the fatigue level. False positives (FP) occur when activities are incorrectly labeled as belonging to the fatigue level, and false negatives (FN) occur when activities are incorrectly labeled as not belonging to the fatigue level. This can be further generalized for multi-class classification as well. Accuracy reflects the model's ability to correctly detect the fatigue level of activities. Sensitivity and specificity indicate the model's ability to recognize different fatigue levels, while precision measures the reliability of the model's predictions of fatigue levels.