## 4 Discussion
### Discussion on the Experimental Results
As described in Section 2.3.2, this table shows the results of analysis with the sum of sensor values (LSUM, RSUM, ASUM) among the FSR sensing data measured when the experimenter is standing with a heavy object. This is the result of the experiments considering that it is common for construction workers to lift heavy objects with one or both hands while standing with their feet on the floor for a while. Of course, in some cases, pressure may not be applied evenly to both feet. Therefore, the data values measured from \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Algorithms** & **A** & **B** & **C** & **D** & **E** & **Average** \\ \hline
**Logistic Regression** & 65.0\% & 89.3\% & 68.0\% & 91.1\% & 72.0\% & 77.1\% \\ \hline
**Linear Regression** & 42.4\% & 66.6\% & 29.1\% & 69.4\% & 24.9\% & 46.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The average of weight classification accuracy by using logistic and linear regression classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Algorithms** & **A** & **B** & **C** & **D** & **E** & **Average** \\ \hline
**Decision Tree** & 89.4\% & 94.7\% & 85.3\% & 95.2\% & 87.2\% & 90.3\% \\ \hline
**Random Forest** & 90.0\% & 94.7\% & 86.6\% & 95.2\% & 88.1\% & 90.9\% \\ \hline
**GBM** & 87.3\% & 94.0\% & 83.7\% & 94.6\% & 85.6\% & 89.0\% \\ \hline
**Light GBM** & 89.4\% & 94.7\% & 86.0\% & 95.1\% & 87.2\% & 90.5\% \\ \hline
**Average** & 89.0\% & 94.5\% & 85.4\% & 95.0\% & 87.0\% & 90.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average of weight classification accuracy of the developed ML-based analysis model. the left and right FSR sensors attached to both insoles may not be uniform. However, the sum of the pressure delivered to both feet can be said to be a result of direct reflection of the weight of the heavy object. Therefore, it is more reasonable to use the sum of pressure sensor values (LSUM, RSUM, or ASUM) obtained from the FSR sensors attached to both insoles to distinguish the weight the experimenter is holding. However, it is effective to use the 14 channels of FSR sensing data values obtained from safety shoes in order to distinguish a moment when a construction worker lifts or unloads a heavy object from other moving actions. Table 8 shows the results of classification analysis with four ML-based classification algorithms for distinguishing between the experimenter's standing and moving act. In this analysis, all data obtained by the 14 channels of FSR sensors were utilized, since the purpose of the analysis is to classify the movement act of an experimenter from the standing act while lifting a heavy object. As shown in Table 8, it is found that random forest has the highest accuracy, at 99%, among ML-based classification algorithms for distinguishing when an experimenter is moving and when the experimenter is standing still. It can be seen that the remaining classification algorithms also show high accuracy of over 97% on average. The result shows that it is not a problem for the algorithm to distinguish when a construction worker is lifting a heavy object.