## 4 Discussion  
In this study, a novel machine learning-based protocol was proposed to predict lower extremity and L5/S1 moments during symmetrical lifting using plantar pressure as an input. This study designed a structure that combines SVM, a classifier, and LSTM model, one of the time-series prediction deep learning models. The performance of the SVM-LSTM model showed rRMSE (%) of 8.06-13.88, and peak moment error rate (%) of 3.20-8.77 (Tables 3 and 5). Recently, cases of estimating the moment of the L5/S1 during lifting using wearable sensor data and human body modeling showed rRMSE of 8-10% and peak moment error rate of 5-10% by wearing 6-17 IMU sensors and two portable force plate or insole sensors [19; 38]. These could be judged to have similar accuracy to results of this study, which estimated the lower extremity and L5/S1 moments during symmetrical lifting using  
Figure 4: Representative results of lower extremities and L5/S1 joint moment prediction (ankle, knee, hip and L5/S1) from the reference system and the SVM-LSTM model. 2 inside sensors and the SVM-LSTM hierarchical machine learning model. According to [PERSON] (2020), the architecture presented in this study is a system that wears two insoles [17]. It is expected to show higher applicability to the working environment than a system that wears 6-19 wearable sensors (e.g., IMU sensors) [17]. [PERSON] et al. (2018) developed a model that predicts the lower extremity moment during weightlifting operation using artificial intelligence and optical marker trajectories. As a result, the error rate of peak moment was about 5 to 16% [40]. Considering the similarity between weightlifting and symmetric lifting, the deep learning-based model proposed in this study showed similar results with a small number of sensors, indicating that the model proposed in this study could be more applicable to the field in terms of convenience than models proposed in previous studies (Table 3). The proposed deep learning-based moment prediction model consists of a hierarchical structure, which is a step of classifying posture (stop and square) and a step of estimating the moment. Collected data were analyzed by dividing the lifting operation into three types of conditions (speed, weight, and posture). As a result, the foot pressure which was the input of the proposed model showed a clear difference according to posture in the cumulative pressure distribution of 1 trial based on the result classification model composed the posture among three types of conditions as the output of the classification model [41]. According to results of this study, the knee joint showed a high rRMSE (4.68%) reduction compared to other joints for the predicted moment between the SVM-LSTM model and the single LSTM model. [PERSON] et al. (2012) have reported that the hierarchical architecture is highly likely to improve the performance due to reduced data complexity [42]. However, in the case of the ANN-LSTM model compared with the single LSTM model, rRMSE and correlation coefficient showed no significant performance improvement. This was because classification accuracy of ANN was 83.5 \(\pm\) 4.5%, which was low compared to accuracies of other RF and SVM classification models. This might be due to the accumulation of errors due to misclassification of the ANN classification model. Hierarchical architecture can be applied as a method to improve performance when a single model does not show high performance due to complexity of input/output data. However, errors can accumulate depending on the performance of the classification model. Thus, it is necessary to consider the performance of the classification model. In this study, Bayesian optimization was used to obtain the optimal sensor location information from the acquired 99 pressure sensor data.