## 2 Materials and Methods
### Data Analysis  
The classification model of symmetric lifting posture was evaluated for classification accuracy rate. The performance of the regression model was evaluated with RMSE, Pearson correlation coefficient (R), and rRMSE using moments of the lower extremities and L5/S1 obtained from the OTS as a reference system. RMSE, rRMSE, and R are calculated with Equations (3)-(5), respectively:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-y_{i}^{*}\right)^{2}} \tag{3}\]  
\[\text{R}=\frac{\sum_{i=1}^{N}\left(y_{i}-\overline{y_{i}}\right)\left(y_{i}^{* }-\overline{y_{i}^{*}}\right)}{\sqrt{\sum_{i=1}^{N}\left(y_{i}-\overline{y_{i} }\right)^{2}\left(y^{*}{}_{i}-\overline{y_{i}^{*}}\right)^{2}}} \tag{4}\]  
\[rRMSE=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-y_{i}^{*}\right)^{2}}}{ \text{max}(y_{i})-\text{min}(y_{i})}\times 100\% \tag{5}\]  
where \(y_{i}\) represents the actual value, \(y_{i}^{*}\) represents the predicted value from the artificial intelligence model, and \(N\) represents the length of 1 trial. In Equation (2), R represents the value of the Pearson correlation coefficient. \(\overline{y_{i}}\) is the average of the actual values, \(\overline{y_{i}^{*}}\) is the average of the values predicted by the artificial intelligence model, \(\text{max}(y_{i})\) is the maximum value in 1 trial, and \(\text{min}(y_{i})=\) is the minimum value in 1 trial. One-way analysis of variance (ANOVA) was performed to compare the accuracy of the single LSTM and the proposed artificial intelligence model. After the analysis, post hoc [PERSON]'s method was used. The significance level was set at \(p\) < 0.01. All statistics analyses were performed using PASW Statistics 18 (Ver. 18, SPSS Inc., Chicago, IL, USA).  
For moments obtained from the proposed model and the reference system, the maximum value among absolute values of moments was extracted for each trial. The error rate between the extracted peak moment value of the proposed model and the reference system was calculated using the following Equation (6):  
\[Error\ rate(\%)=\frac{\text{\it Peak\ {}}_{\text{\it Model}}-\text{\it Peak\ {}}_{\text{\it ref}}}{\text{\it Peak\ {}}_{\text{\it ref}}}*100\% \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**LSTM**} & \multicolumn{2}{c}{**ANN-LSTM**} & \multicolumn{2}{c}{**RF-LSTM**} & \multicolumn{2}{c}{**SVM-LSTM**} \\ \cline{3-8}  & & & **Squat** & **Stoop** & **Squat** & **Stoop** & **Squat** & **Stoop** \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Regression \\ Structure \\ \end{tabular} } & Bi-LSTM layer1 Node & 103 & 610 & 88 & 625 & 211 & 104 & 168 \\  & Dropout layer & 0.172 & 0.120 & - & 0.101 & - & - & 0.267 \\  & Bi-LSTM layer2 & 103 & 439 & - & 387 & - & - & 167 \\  & Node & 0.172 & 0.1203 & - & 0.101 & - & - & 0.267 \\  & Dropout layer & 31 & 316 & - & 240 & - & - & 166 \\  & Fully connected layer1 & 43 & 20 & 50 & 26 & 36 & 17 & 31 \\  & Dropout layer & 0.172 & 0.120 & 0.386 & 0.101 & 0.136 & 0.149 & 0.267 \\  & Fully connected layer2 & 22 & 10 & 25 & 13 & 18 & 9 & 16 \\  & Fully connected layer3 & 4 & 4 & 4 & 4 & 4 & 4 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Training \\ Training \\ \end{tabular} } & Momentum & 0.920 & 0.503 & 0.812 & 0.530 & 0.701 & 0.643 & 0.709 \\  & L2 regularization & \(8.49\times 10^{-4}\) & \(6.28\times 10^{-3}\) & \(8.41\times 10^{-3}\) & \(7.16\times 10^{-3}\) & \(9.07\times 10^{-3}\) & \(8.45\times 10^{-3}\) & \(4.84\times 10^{-3}\) \\  & \(\text{\it Initial learning rate}\) & \(2.67\times 10^{-3}\) & \(1.87\times 10^{-3}\) & \(8.41\times 10^{-3}\) & \(9.27\times 10^{-4}\) & \(4.89\times 10^{-3}\) & \(6.75\times 10^{-3}\) & \(2.78\times 10^{-3}\) \\  & Input weights & \(\text{\it Inpuf}\) & \(\  
where _Peak Model_ was the peak value among absolute values of prediction values of the artificial intelligence model, and _Peak ref_ was the peak value among absolute values of the reference moment. _Error rate_(%) meant the error rate between peak moment.