Smart sewing work measurement system using IoT-based power monitoring device and approximation algorithm

Woo-Kyun Jung, Hyungjung Kim, Young-Chul Park, Jae-Won Lee and Sung-Hoon Ahn

International Journal of Production Research, 2020, vol. 58, issue 20, 6202-6216

Abstract: To enable Small and Medium-sized Enterprises (SMEs) level garment manufacturers to measure and monitor the work of individual workers without incurring a large financial burden, a smart sewing work measurement system was developed using an IoT-based power monitoring device and an approximation algorithm. The amount of electric current used in the sewing work was measured, and the measured data was transmitted to a server using Wi-Fi communication. The analysis of the data was conducted through the Symbolic Aggregate approximation (SAX) and Dynamic Time Warping (DTW) methods. The daily workload of each worker derived from the system developed through this study showed an error rate of 8% compared to the actual workload, and the time measured by the sensor was different from the time measured by a manager using a stopwatch. These differences are considered to be due to measurement errors in the stopwatch, human noise from the measurer and the operator, and the relatively few samples relative to the total workload. The IoT-based power monitoring and work measurement system for sewing work developed through this study can be applied to smart garment manufacturing factories at an acceptable cost level, while SMEs can realise high recognition rates and semi-real-time monitoring.

Date: 2020
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DOI: 10.1080/00207543.2019.1671629

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