###### Abstract  
In the Korean construction industry, legal and institutional safety management improvements are continually being pursued. However, there was a 4.5% increase in the number of workers' deaths at construction sites in 2017 compared to the previous year. Failure to wear safety helmets seems to be one of the major causes of the increase in accidents, and so it is necessary to develop technology to monitor whether or not safety helmets are being used. However, the approaches employed in existing technical studies on this issue have mainly involved the use of chinstrap sensors and have been limited to the problem of whether or not safety helmets are being worn. Meanwhile, improper wearing, such as when the chinstrap and harness fixing of the safety helmet are not properly tightened, has not been monitored. To remedy this shortcoming, a sensing safety helmet with a three-axis accelerometer sensor attached was developed in this study. Experiments were performed in which the sensing data were classified whether the safety helmet was being worn properly, not worn, or worn improperly during construction workers' activities. The results verified that it is possible to differentiate among wearing status of the proposed safety helmet with a high accuracy of 97.0%. construction; worker safety; safety helmet; three-axis accelerometer sensor; data mining Article  
Safety Helmet Wearing Management System for Construction Workers Using Three-Axis Accelerometer Sensor  
[PERSON], [PERSON], [PERSON], S.  
These results indicate that it is very important to manage the wearing of safety helmets, which can protect the heads of workers, as there is a high possibility that the head will be the body part that first impacts the ground in a fall accident [3; 4]. When safety helmets are properly worn, they provide shock absorption, which reduces the force transmitted to the head of the wearer upon object impact to less than 10%, thus enhancing worker safety. Aside from falls, helmets also reduce the danger from accidents involving falling objects, flying objects, impacts, electrical shocks, and so on. Therefore, numerous institutional and technical studies have been conducted on increasing validity and accuracy when determining whether or not safety helmets are being worn properly [5; 6; 7; 8; 9; 10; 11]. In terms of technology, the read sensors have been attached to chinstraps in previous studies to detect emergencies based on such information such as whether or not the helmet is being worn and its position. In those studies, it was determined whether or not the helmet was being worn by using sensors attached to chinstraps or additional sensing data such as altitude and position information. For example, chinstrap sensors and altitude sensing data have been used to confirm that the helmet was being worn [9; 10; 11]. However, the previous studies in which sensors were attached to chinstraps were limited in that it is difficult to determine whether or not a helmet is being worn when looking at the movements of a worker in a work environment who fastens the chin strap but does not wear the helmet, carries or wears the helmet on his or her body, or performs other similar activities. The previous studies suggest that additional sensing data such as altitude and position information are necessary since using only the sensors attached to chinstrap is not enough to classify whether or not a safety helmet is being worn [9; 10; 11]. The objective of this research was to resolve the shortcomings of previous studies by developing a system using a three-axis accelerometer sensor to identify the activities of workers in the work area of a current construction site based on their movement data and to determine not only whether or not safety helmets are being worn, but also whether they are being worn improperly.