# Remote Sensing of Sewing Work Levels Using a Power Monitoring System
## 2 Literature Review  
The objective of this study is to quantify the skill level of a sewing machine operator and the difficulty of sewing work using a power monitoring system. Research on the power monitoring of sewing machines has been conducted for a long time [4] and has until recently focused on reducing the energy used in the production of a garment [5; 6; 7]. In addition, the use of a tabogram or stitching monitoring has been studied to extract sewing work information from the operation of a sewing machine [8; 9]. However, these methods are limited in application, as they require the modification of the sewing machine or the attachment of additional equipment. Various attempts have been made to measure the sewing operator's performance. [PERSON] et al. conducted a time study on the operator's working time and a study based on self-recorded data of the operator [10]. [PERSON] et al. and [PERSON] et al. measured the operator's working time using a stopwatch [11; 12]. Meanwhile, an attempt to analyze the load of the sewing operation by measuring and analyzing the operating time was also made [13; 14]. In addition, digital image analysis or work measurement based on vision technology has been attempted [15; 16; 17]. Studies have also been conducted on a method of simultaneously analyzing time and motion [18]. A measurement of time called the standard minute value (SMV) is used as a reference time for the required work. The SMV is a numerical value that represents the standard time required for a process or operation in a standard environment for a standard worker [19]. [PERSON] and [PERSON] studied ways to effectively improve the SMV in the garment manufacturing process [3], and [PERSON] and [PERSON] studied how to measure the SMV to improve the productivity of sewing lines [20]. [PERSON] investigated an approach to improving sewing process reliability through SMV sampling studies [2]. [PERSON] et al. proposed a method to increase the productivity and efficiency of the garment production process through time studies [21]. Under these approaches, the skill level of the sewing machine operator and the difficulty of work are determined based on the SMV. However, the determination of SMV criteria can be problematic as the number of samples is small, human measurement errors are inevitable, and factors related to product quality are not reflected in the measurement. Various studies on the performance measurement and monitoring of the sewing process have also been conducted using other methods. [PERSON] et al. predicted sewing performance by applying an artificial intelligence technique to sewing work [22], and [PERSON] and [PERSON] presented a neural network-based model to solve the problems associated with the existing SMV measurement method [23]. Finally, [PERSON] et al. monitored the performance of garment factory workers using a radio frequency identification (RFID) system [24]. Recently, various approaches have also been evaluated to inform the placement of operators in the manufacturing process [25; 26]. [PERSON] presented expert estimates using fuzzy logic to select machine operators for complex manufacturing processes [27]. [PERSON] et al. presented a manufacturing line balancing problem that takes into account the skill of the operator in a fuzzy environment [28]. [PERSON] et al. attempted to reduce errors in a manufacturing process by applying ergonomic elements and Bayesian networks [29]. The level of skill of the operator and level of difficulty of the sewing work are important factors to maximize the efficiency by balancing the production line. These factors should be considered simultaneously in terms of time and quality, and can be optimized by achieving the optimal arrangement considering the skill level of the operator and difficulty level of the sewing work according to the quantified criteria.