## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**Amalic**} & \multicolumn{3}{c}{**Knee**} & \multicolumn{3}{c}{**Hip**} & \multicolumn{3}{c}{**LSS1**} \\ \cline{3-14}  & **r** & **RMSE** & **rRMSE** & **rRMSE** & **rRMSE** & **rRMSE** & **rRMSE** & **rRMSE** & **rRMSE** & **rRMSE** \\  & **r** & **(m/BW)** & **(s)** & **r** & **(m/BW)** & **(s)** & **r** & **(m/BW)** & **(s)** & **r** & **(m/BW)** & **(s)** & **r** & **(m/BW)** & **(s)** \\ \hline \multirow{3}{*}{Squart} & LSTM & 0.85 & 0.065\(\pm\)0.007 & 12.19 \(\pm\) 3.21 & 0.83 & 0.150 \(\pm\) 0.001 & 18.64 \(\pm\) 2.18 & 0.94 & 0.127 \(\pm\) 0.017 & 9.38 \(\pm\) 1.40 & 0.96 & 0.198 \(\pm\) 0.021 & 9.95 \(\pm\) 0.027 \\  & NN+LSTM & 0.87 & 0.052 \(\pm\) 0.005 & 11.72 \(\pm\) 2.04 & 0.79 & 0.167 \(\pm\) 0.003 & 20.58 \(\pm\) 5.80 & 0.95 & 0.15 \(\pm\) 0.016 & 8.81 \(\pm\) 3.23 & 0.94 & 0.174 \(\pm\) 0.024 & 9.23 \(\pm\) 1.44 \\  & NN+LSTM & 0.59 & 0.058 \(\pm\) 0.008 & 10.69 \(\pm\) 0.86 & 0.87 & 0.126 \(\pm\) 0.002 & 16.33 \(\pm\) 2.97 & 0.96 & 0.103 \(\pm\) 0.024 & 8.08 \(\pm\) 1.53 & 0.95 & 0.147 \(\pm\) 0.023 & 8.50 \(\pm\) 1.87 \\  & SVM+LSTM & 0.93 & 0.048 \(\pm\) 0.007 & 10.48 \(\pm\) 1.65 & 0.91 & 0.121 \(\pm\) 0.002 & 14.03 \(\pm\) 3.23 & 0.96 & 0.112 \(\pm\) 0.003 & 8.08 \(\pm\) 1.49 & 0.95 & 0.317 \(\pm\) 0.025 & 8.44 \(\pm\) 1.30 \\ \hline \multirow{3}{*}{Sloop} & LSTM & 0.94 & 0.052 \(\pm\) 0.005 & 11.18 \(\pm\) 1.75 & 0.88 & 0.155 \(\pm\) 0.002 & 18.70 \(\pm\) 4.01 & 0.94 & 0.123 \(\pm\) 0.017 & 9.26 \(\pm\) 1.04 & 0.94 & 0.194 \(\pm\) 0.029 & 10.40 \(\pm\) 0.75 \\  & NN+LSTM & 0.86 & 0.059 \(\pm\) 0.009 & 12.66 \(\pm\) 3.21 & 0.83 & 0.153 \(\pm\) 0.002 & 18.36 \(\pm\) 4.32 & 0.95 & 0.136 \(\pm\) 0.0043 & 9.99 \(\pm\) 1.67 & 0.93 & 0.208 \(\pm\) 0.036 & 1.210 \(\pm\) 2.14 \\  & NN+LSTM & 0.93 & 0.049 \(\pm\) 0.007 & 10.12 \(\pm\) 2.36 & 0.90 & 0.125 \(\pm\) 0.004 & 15.36 \(\pm\) 3.11 & 0.96 & 0.110 \(\pm\) 0.023 & 7.91 \(\pm\) 1.90 & 0.95 & 0.178 \(\pm\) 0.027 & 9.64 \(\pm\) 1.82 \\  & SVM+LSTM & 0.94 & 0.048 \(\pm\) 0.009 & 10.22 \(\pm\) 3.17 & 0.92 & 0.117 \(\pm\) 0.003 & 13.75 \(\pm\) 2.51 & 0.96 & 0.113 \(\pm\) 0.028 & 8.02 \(\pm\) 2.47 & 0.96 & 0.173 \(\pm\) 0.021 & 8.81 \(\pm\) 1.17 \\ \hline \end{tabular}
\end{table} 
Table 5. RMSE, rRMSE and R (correlation coefficients) values between calculated joint moment from optical tracking system and predicted joint moment with a hierarchical deep learning model. Figure 3 shows performances of single LSTM, ANN-LSTM, RF-LSTM, and SVM-LSTM models by rRMSE. The SVM-LSTM model showed significant differences (\(p<0.01\)) in ankle and L5S1 joints compared with single LSTM and ANN-LSTM models. In addition, for the knee joint, the SVM-LSTM model showed a significant difference (\(p<0.01\)) compared with all other models. Compared to the single LSTM model, the SVM-LSTM model showed approximately 2.19%, 4.68%, 1.23% and 1.35% rRMSE reductions for the ankle, knee, hip, and L5/S1, respectively. In particular, the knee joint moment showed the largest decrease in rRMSE compared to other joints. Figure 4 shows the moment predicted by the single LSTM model, the SVM-LSTM model and representative values of the reference moment classified according to posture. The \(x\)-axis is the time normalized to 100% during the lifting task and the \(y\)-axis is the moment normalized by the weight of each subject. (\(-\)) on the \(y\)-axis means flexion moment and (\(+\)) refers to the extension moment. During the lifting task, changes in the moment of the knee joint showed a different tendency from those of the ankle, hip joint, and lumbar joint. In the squat posture, the peak extension moment at the knee joint occurred at the beginning of the lifting motion and showed a pattern of decreasing close to zero. In stoop posture, the peak flexion moment occurred initially, and the flexion moment tended to decrease as the lifting task progressed.