# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 5 Conclusions and Future Works  
In this study, a new method of remotely measuring sewing work using a power monitoring system was presented. The results of experiments using the proposed remote sensing method made it possible to derive each operator's \(L_{S}\) and the \(L_{D}\) of each work task according to the measured work time and observed quality. The results indicate considerable differences between the expert-assigned and numerically determined levels: The weight factor for \(L_{S}\) proposed by the experts was about 15% lower than that indicated by the experimental results, and the weight for \(L_{D}\) proposed by the experts was about 15% to 40% higher than that indicated by the experimental results. Although the experiment performed in this study was conducted over two days, the results of 30 tasks of 5 difficulty levels, performed by each operator, were analyzed. In addition, it can be determined that the power monitoring system can produce reliable results through data analysis of approximately 1000 sewing machines. Note, however, that the weight factors presented as the results of this experiment are those determined under specific conditions, and therefore, may not be applicable to all situations. Nevertheless, if we continue to increase the number of samples collected under the same conditions, the weight factors of \(L_{S}\) and \(L_{D}\) will continue to change and eventually converge to the appropriate values. While there is a limit to the number of samples that can be effectively collected by manual measurements, the method proposed in this study allows for the collection of a large number of measurements while the operators perform their work without knowing they are being measured. Thus, the proposed method can continuously obtain more accurate information simply by increasing the number of \(L_{S}\) and \(L_{D}\) samples collected. The results of this study suggest a new method that overcomes the limitations of the existing method, which was simply representative of the operating  
Figure 8: Weight factor trends: (**a**) \(L_{S}\) weight factor trend from the test results; (**b**) \(L_{D}\) weight factor trend from the test results. time by measuring an operator's specific operating time with a low sample. When this system is applied, the operator's performance is accumulated as big data as the work progresses, enabling more accurate analysis. This not only overcomes the limitations of manual measurement, but also allows accurate \(L_{S}\) and \(L_{D}\) measurements to be secured in the database. In addition, the database thus created can be applied to not only accurately predict the production cost and time for orders, but also to directly improve production. As previously discussed, the results of this study can be applied to the optimization of garment production lines. By considering the quantified \(L_{S}\) and \(L_{D}\) when placing operators on the garment production line, it is possible to construct an optimized production line, which ensures maximum production. Studies on optimizing production lines can be examined to relocate operators or reorder tasks, and the results of this study can provide a more effective and accurate alternative to weighting in this process. In future research, if the proposed sewing work remote sensing method is applied to an actual production line using a power monitoring system, the \(L_{S}\) and \(L_{D}\) can be calibrated in real time to inform the arrangement of sewing line workers, helping to increase the productivity of the garment production line. Conceptualization, W.-K.J. and E.S.S. ; methodology, W.-K.J. and E.S.S. ; software, W.-K.J. ; validation, E.S.S. and Y.-C.P. ; formal analysis, W.-K.J. ; resources, J.-W.L. ; data curation, W.-K.J. ; writing--original draft preparation, W.-K.J. ; writing--review and editing, E.S.S. ; visualization, W.-K.J. ; supervision, E.S.S. ; project administration, E.S.S. ; funding acquisition, Y.-C.P. All authors have read and agreed to the published version of the manuscript.