## 4 Discussion
According to a previous study, the main change direction of the pressure center of the foot during symmetric lifting was the anterior/posterior direction, and a constraint was given to select the number of at least 1 sensor in each area to select the sensor position for the entire range of the foot [43]. When the subject wears an inside sensor, the position and absolute value at which the sensor is pressed are different depending on the position of the sensor and the size of the foot. Thus, a large number of sensors was selected. Additionally, in this study, the area of the foot was divided into 3 zones (forefoot, midfoot, rearfoot) and the sensor position was extracted for each area. In the case of a different task, for example, an asymmetric lifting, it is necessary to consider a method of selecting an optimal position by setting the region differently according to the position of the pressed sensor. As an objective function, the error rate of the classification model and the rRMSE of the regression model were calculated by giving 7 and 3 weights, respectively. This is because, in the case of a model using a hierarchically structured protocol such as in this study, the error in the classification model has a structure in which the error in the subsequent regression model can be accumulated. To minimize the misclassification rate, the ratio of 7 to 3 was chosen. However, if weights of the objective function for the classification model and the regression model are different, the selection of features might be different. The purpose of this study is to propose a system that can predict the joint moments of the lower extremities and L5/S1 in the workplace. The proposed system is meaningful in overcoming the spatial constraints of existing studies. It is considered that this system can be applied to workers who are engaged in an irregular working environment. For example,parameters show a 13 times higher risk of musculoskeletal disorders than other medical workers, mainly due to frequent lifting of patients [44]. In addition, agricultural workers are also exposed to musculoskeletal disorders caused by lifting work, carrying heavy objects, or improper posture [45; 46]. Based on the system proposed in this study, a prediction of the lower extremities and L5/S1 joint moment might provide a continuous health notification for workers who do not have an irregular working environment. Therefore, the risk of musculoskeletal disorders can be reduced by applying the health notification which includes the system proposed in this research. This study has several limitations. First, the data used for training the model consisted of only the data of young adult males. However, to the best of our knowledge, this is the first study to predict moments of the lower extremities and lumbar spine during work-related lifting motions using insole sensors and machine learning. In future research, it is necessary to include the various age groups in order to increase the usefulness of the model. Second, a model was constructed for limiting the lifting task. In this study, models were constructed for the squat and stoop posture during symmetric lifting. In future research, it is necessary to construct a generalized model for lifting tasks by adding data to symmetric lifting posture such as free lifting, semi-squat, and asymmetric lifting tasks. Lastly, a limitation of this study is that it was conducted with limited number of healthy adults. The subjects' musculoskeletal disorders were defined by their opinion without any medical history confirmation. Consequently, laterality or underlying physical condition was not considered. In future study, obtaining more data while considering laterality and medical history is necessary to construct a more robust machine learning model. In conclusion, we proposed an architecture that could predict the lower extremity and L5/S1 moments as plantar pressure during symmetric lifting.