• Journal of the Korean Society for Precision Engineering
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• v.33 no.5
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• pp.371-376
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• 2016

Safety related workers, such as firefighters, have to wear a protective helmet. The Development of Helmets for safety is in progress to promote the wearable device industry. Several accidents caused by negligence in recent days have raised public attention to safety. For this reason, the amount of national budget funding for the study of fire-fighting and smart safety helmets has increased. However, most previous studies have focused on safety, rather than the application of new technologies based on physical attributes, especially the characteristics of head shape and size, even though fire helmets play the critical role of protection from flames and external shocks etc. in an emergency. This paper will present the smart technologies and newly developed designs for safety helmets that are personalized for each firefighter, based on the characteristics of their head, and will help a rescue operation to be much more safe and efficient.

• 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.

• 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 Korean Society of Safety
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• v.35 no.2
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• pp.94-99
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• 2020

Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.61-68
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• 2019

The goal of the construction work in the past was to improve the efficiency through cost reduction and shortening the construction period. However, the importance of safety management has recently been emphasized, and the Ministry of Employment and Labor has set a goal of reducing the number of deaths caused by industrial accidents by half. As a result, institutional and legal improvements are pursued and the need for safety is emphasized. However, most construction companies, except for some large construction companies, are lacking in safety management. So, In this paper, we design and implement a safety management system for construction site using smart helmets and beacons.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.82-89
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• 2023

Due to the characteristics of ship structure, the compartment structure is complicated and narrow, so safety accidents frequently occur during the work process. The main causes of accidents include structural collisions, falling objects, toxic substance leaks, fires, explosions, asphyxiation, and more. Understanding the on-site conditions of workers during accidents is crucial for mitigating damages. In order to ensure safety, the on-site situation is monitored using CCTV in the ship, but it is difficult to prevent accidents with the existing method. To address this issue, a smart safety helmet equipped with location identification and voice/video communication capabilities is being developed as a safety technology. Additionally, the smart safety helmet incorporates environmental sensors for temperature, humidity, vibration, noise, tilt (gyro sensor), and gas detection within the work area. These sensors can notify workers wearing the smart safety helmet of hazardous situations. By utilizing the smart safety helmet and environmental sensors, the safety of workers aboard ships can be enhanced.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.119-120
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• 2023

This paper investigates the applicability of movable sensors that can be attached to hard hats to protect construction site safety management to prevent safety accidents based on accident case studies in the field of construction engineering and the gas sensors currently used in construction sites. We would like to propose MQ-2, a standard Arduino gas sensor.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.433-440
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• 2022

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.163-170
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• 2023

With the introduction of the law regarding severe penalties for major accidents, employers, management executives, and corporations have significantly increased the number of safety managers and invested extensively in acquiring ISO certifications to prevent accidents in industrial sites. Moreover, the implementation of the Smart Safety Management System (SSMS) has facilitated the management of personnel and safety equipment. While IoT-based management systems have been applied to safety gear such as helmets, safety harnesses, and protective clothing, the responsibility for safety shoes still primarily lies with on-site managers and individuals, leaving a vulnerability to accidents. In this study, we aim to implement a Raspberry Pi-based sensor device to proactively detect workers' safety shoe usage upon entering the site. The goal is to confirm the usage of safety shoes and prevent accidents that may occur due to non-compliance with safety shoes regulations.