• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.281-282
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• 2022

현재 전동 킥보드 헬멧 미착용으로 인한 사고가 끊임없이 야기되고 있다. 개인형 이동장치 이용자 수가 증가함에 따라 법 개정을 통하여 헬멧 착용이 의무 사항이지만 여전히 낮은 착용률을 나타내고 있다. 본 논문에서는 모든 공유 킥보드 회사에서 사용 가능한 딥러닝 기반의 전동킥보드 헬멧 착용 인식시스템을 제시한다. 타 공유 전동킥보드 회사 앱에서 본 논문의 결과물을 사용할 때는 사용자가 타사 앱에서 헬멧 인식 요청 시 자사 앱에서 헬멧 착용 여부를 인식하여 결과를 전송한다. 자사 앱 사용자는 인식 기록을 조회할 수 있고, 타사 관리자는 사용자의 정보를 조회 및 관리할 수 있다. 본 시스템을 통해 전동킥보드 이용 시 헬멧 착용을 장려하여 착용률 증가와 사고 시 인명피해 감소를 기대한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.39-51
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• 2017

Recently, looking at construction sites with either large or small scale, accidents like collision, fall, etc. occur often. These accidents lead to not only damage of human lives but also serious economic loss. In case of large scale constructions sites, safety management systems are used to reduce industrial accidents. However in construction sites with small scale, those systems cannot be applied due to problems such as lack of compatability and high installation expense. In this case, just by putting on safety gears can also reduce industrial accidents. Therefore, in this paper, a safety management systems that can be used at both large and small scale construction sites is proposed. This safety management system consists of a smart module, a repeater and a gateway, and a monitoring system. The smart module, which is detachable, is attached to a safety helmet. This module transfers the current status of the user to the monitoring system through the repeater and the gateway. The repeater transfers the data received from the smart module to the gateway, and the gateway sends the data from the repeater to the monitoring system. The monitoring system shows the user status to the safety supervisor by displaying the data - temperature, height, intensity of illumination, images - received from the smart module. The safety supervisor can monitor the user status in real-time and take immediate action in case of emergency through this monitoring system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1612-1617
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• 2020

Recently, research to reduce the accident rate by actively adopting artificial intelligence technology in the field of disaster safety technology is spreading. In particular, it is important to quickly search the Rescue Requester in order to effectively perform rescue activities at the disaster site. However, it is difficult to search for Rescue Requester due to the nature of the disaster environment. In this paper, We intend to develop an artificial intelligence system that can be operated in a smart helmet for firefighters to search for a rescue requester. To this end, the optimal SoC was selected and developed as an embedded system, and by testing a general-purpose artificial intelligence S/W, the embedded system for future smart helmet research was verified to be suitable as an artificial intelligence S/W operating platform.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.167-175
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• 2022

With the recent expansion of electric kickboards and bicycle sharing services, more and more people use them. In addition, the rapid growth of the delivery business due to the COVID-19 has significantly increased the use of two-wheeled vehicles and personal mobility. As the accident rate increases, the rule related to the two-wheeled vehicles is changed to 'mandatory helmets for kickboards and single-person transportation' and was revised to prevent boarding itself without driver's license. In this paper, we propose a wearable smart safety equipment, called SafetyHelmet, that can keep helmet-wearing duty and lower the accident rate with the communication between helmets and mobile devices. To make this function available, we propose a safe driving assistance function by notifying the driver when an object that interferes with driving such as persons or other vehicles are detected by applying the YOLO v5 object detection algorithm. Therefore it is intended to provide a safer driving assistance by reducing the failure rate to identify dangers while driving single-person transportation.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.371-380
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• 2019

Electromygram was measured in three different cases; not wearing of a helmet, wearing of a 920g helmet, and wearing of a 1310g helmet, and it was revealed that there was a high level of muscle activities on the opposite side (sternocleidomastoid muscle) while the head and the neck were moving right and left and muscle activities of the curve and of the hyperextension were found to be not significant. Especially, the presence of a helmet seemed to cause a difference in a level of muscle activities on the deltoid and erector spine muscles while lifting or lowering with a weight placed on the deniz backboard. Therefore, it would be possible to assume that this newly developed smart helmet would not affect muscles around the necks of paramedics if they do not move their necks and heads right and left while lowering or lifting a patient using the deniz backboard. In addition, in case of the deltoid and erector spine muscles, it is shown that an increase in the degree of movement could lead to an increase in the level of muscle activities on the muscles controlling of the corresponding action, which are waist and back muscles. Despite it, it would be possible to prevent possible injuries and/or muscular and skeletal diseases around the lumbar by fully complying with a basic rule of straightening of the waist while making a power-lifting motion.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.357-358
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• 2016

현재 교통수단으로 이륜차를 타는 사람들이 점점 많아짐에 따라서 도로에서 많은 사고가 발생하여 사망률이 증가하고 있다. 이에 따라서 사망률을 줄이기 위한 해결방법으로 스마트 헬멧을 착용하여 현재 사고자의 위치를 문자 메시지를 통해 경찰이나 병원, 가족들에게 자동으로 문자를 발송하여 현재 위치정보를 파악하여 사망률을 줄이는 것을 목적으로 하고 있다.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.48-56
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• 2023

This paper implements many-to-many communication between users and develops a multi-functional smart helmet for worker protection and environmental safety in the shipbuilding and shipping industry. First, the communication situation is recorded in the field to perform signal processing for noise that interferes with communication. Then, it deals with the contents of developing smart helmets, data acquisition, algorithms, and simulations. The simulation results analyzed by applying the adaptive algorithm are shown, and their usefulness is confirmed. In conclusion, looking at the optimization process for the convergence factor of the Least Mean Square and Filtered-x Least Mean Square Adaptation Algorithm was possible. It is thought that it has laid the foundation for implementing many-to-many communication, the function of smart helmets that reduces or removes various noises at the shipyard in the future.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.28-36
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• 2022

Currently, the automation and intelligence of the shipbuilding industry have improved its work production capacity and cost competitiveness, but the reduction rate of safety accidents among industrial site workers is still low and the damage caused by safety accidents is very serious, so there is a need for improvement according to the workplace. This research aims to demonstrate the connectivity between smart safety helmets in the demonstration area to verify the effectiveness along with the development of smart helmets for worker protection and environmental safety in shipyards. For efficient communication between workers, impact noise of over 95dB was confirmed in the workplace, and noise reduction was required. To solve this problem, the filtering performance was compared and analyzed using the Butterworth, Chebyshev, and elliptic algorithms. The connectivity test and noise reduction method between smart helmets proposed in this study will increase the usability and safety of the field through the development of advanced smart helmets tailored to the shipbuilding workplace in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1226-1274
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• 2022

In this paper, we dealt with a safety helmet for detecting dangerous situations that focuses on falling accidents and gas leaks, which are the main causes of industrial accidents. the fall situation range was set through gravity acceleration measurement using an acceleration sensor, and as a result, a fall detection rate of 80% could be confirmed. .In addition, the dangerous gas concentration was measured through a gas sensor, and when a digital value of 188 or more was output through a serial monitor, it was determined as a gas dangerous situation, and a fall warning message and a gas warning message could be checked through a smart-phone application produced based on the app inventor program.

• Smart Media Journal
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• v.12 no.3
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• pp.120-128
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• 2023

There are various ICT technologies continuously being developed to reduce damage by industrial accidents. And research is being conducted to minimize damage in case of industrial accidents by utilizing sensors, IoT, big data, machine learning and artificial intelligence. In this paper, we propose a design method for a smart device capable of multilateral communication between devices and smart repeater in the communication shaded Areas such as closed areas of industrial sites, mountains, oceans, and coal mines. The proposed device collects worker's information such as worker location and movement speed, and environmental information such as terrain, wind direction, temperature, and humidity, and secures a safe distance between workers to warn in case of a dangerous situation and is designed to be attached to a helmet. For this, we proposed functional requirements for smart devices and design methods for implementing each requirement using sensors and modules in smart device. And we derived evaluation items for performance evaluation of the smart device and proposed an evaluation environment for performance evaluation in mountainous area.