## 4 Discussion  
### Discussion on the Experimental Results  
The results of the first analysis for the experiments using univariate multiple linear regression found that the correlation between the differential measured weights (experimenter's weight + heavy object weight: 5-20 kg) and the corresponding sensing values (only ASUM data were used) were insufficient because the degree to which the independent variable explains the dependent variable is somewhat low. As shown in Table 4, the \(R^{2}\) values of experimenter E and experimenter C were relatively low at 24.5% and 29.3%, respectively, and the \(R^{2}\) value of experimenter A was 42.2%, which almost approaches the total average of 46.4%. On the other hand, the \(R^{2}\) values of experimenter D and experimenter B were relatively higher at 69.3% and 66.9%, respectively. As shown in the scatter plots in Table 3, the reason why the regression results are not so clearly explained is due to the wide and overlapping distribution of sensor ASUM values measured for each weight. This can be said to be a result of various factors, such as the behavioral characteristics of each experimenter, their posture, shoe size, as well as the grounding status of the  
Figure 3: The accuracy of ML-analysis model developed for distinguishing the actual weights by classification algorithms. sensor-attached insole, all of which affect sensitive sensor values. Therefore, an effective analysis method is required to monitor the actual weight lifted by construction workers using the FSR sensing data. First, we tested the analysis method adopting logistic and linear regression classification algorithms that are conventionally used. The test results are shown in Table 6. The accuracy of classifying weight by logistic regression analysis (77.1%) was found to be somewhat higher than that of weight classification by linear regression analysis (46.5%). However, it was confirmed that the accuracy of the analysis using these two classification algorithms was not satisfactory enough to distinguish the actual weight of the object carried by the experimenter after analyzing the FSR sensing data values. Therefore, this research study has developed a ML-based classification analysis model using Python. This model has been tested and developed by adopting the widely used classification algorithms, which includes decision tree, random forest, GBM, and light GBM. The accuracy of the test results using the ML-based analysis model is as shown in Table 7. In the case of experimenter C, who showed relatively low accuracy in weight estimation compared to the other experimenters, the average accuracy of estimation of applying the four classification algorithms was about 85.4%, and the average accuracy of experimenters B and D, which showed relatively high estimation accuracy, was 94.5% and 95.0%, respectively. In the case of experimenters A and E, who both showed medium accuracy, they had an average estimation accuracy of 89.0% and 87.0%, respectively. Therefore, the weight estimation accuracy according to all experimenters was confirmed to have an average of 90.2%, which corresponds to a relatively high and meaningful level of accuracy. In addition, when looking at the classification accuracy of the weight lifted by each experimenter by each classification algorithm, the accuracy of the GBM classification algorithm for experimenter C is the lowest at 83.7%. On the other hand, it is found that the accuracy by decision tree and random forest for experimenter D is the highest at 95.2%. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order as an experimental result: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). This means that the developed ML-based analysis model can classify the actual weight of the object held by the experimenter with an average accuracy of 90% or more.