• Journal of Trauma and Injury
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• v.31 no.3
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• pp.117-124
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• 2018

Purpose: This study was to analyze clinical and epidemiological characteristics of elderly patients who were admitted to the emergency department (ED) due to falls by separating male and female. Methods: We retrospectively analyzed the fall patients aged 65 years or older from the data of the in-depth surveillance study of injured patients visit to the ED under the supervision of the Korea Centers for Disease Control and Prevention (KCDC) from 2011 to 2016 by separating male and female. Results: A total of 361,588 elderly fall patients were analyzed and, among them, 14,429 (37.3%) were males and 24,208 (62.7%) were females. Male and female showed similar frequency of damage happening season. However, they showed falling accident mostly on winter. The time of injury occurrence is mostly from 12:00 to 18:00 with 4,949 (34.3%) male and 8,564 (35.4%) female. Most falls occurred in daily activities, accounting for 7,614 (52.8%) in males and 14,957 (61.8%) in females, respectively. Unintentional damage accounted for the most part and 7,395 (51.2%) of male and 15,343 (63.4%) of female were injured indoors. Head and neck were the most common site of injuring, with 8,392 (58.2%) in males and 7,851 (32.4%) in females. According to ED examination outcomes, most of the patients were discharged, while the majority of the hospitalized patients were admitted to the general patient room. Conclusions: The elderly falls occurred mostly from 12:00 to 18:00, during winter and to elderly women. Also, they happened unintentionally indoors in everyday life, mostly. Proved clinical, epidemiological characteristics from this research will be used as useful indicator at validity research of development of prevent program of falling accident for elderly people.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.314-320
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• 2024

Objectives: This study aimed to identify the prevalence of falls in community-dwelling older adults and to identify associated factors using the 2019 Community Health Survey. Methods: The original data was from the 2019 Community Health Survey, and the study sample comprised 1,642 older adults aged 65 years and older in Jeju province. Data collection was conducted from August 16 to November 20, 2019, through an interview done by a trained investigator. Respondents were queried about demographic characteristics, riding bicycles, hospital treatment due to an accident or poisoning in the previous year, fall experiences in the past year, fear of falling, self-management status, and pain and discomfort. Multivariate logistic regression analysis was used to evaluate for associations between potential risk factors and falls. Results: The prevalence of falls in this community-dwelling older adults was 13.1%. Falls were associated with riding bicycles (odds ratio = 4.7; 95% confidence interval: 2.26-9.81), fear of falling (odds ratio = 0.3; 95% confidence interval: 0.24-0.49), hospital treatment due to an accident or poisoning in the previous year (odds ratio = 7.8; 95% confidence interval: 5.02-12.19), self-management status (odds ratio = 0.6; 95% confidence interval: 0.34-0.89), and pain and discomfort (odds ratio = 0.6; 95% confidence interval: 0.40-0.87). Conclusions: We found that the prevalence of approximately about 13% of older adults living in a community has experienced falls. Based on the results of the study, we provided primary data to develop the care management intervention program to prevent falls and avoid risk factors that cause falls in community-dwelling older adults.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.3
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• pp.257-264
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• 2024

This study analyzed data from the Korea National Hospital Discharge In-depth Injury Survey (KNHDIS) (2004~2021) and found that for the young-old with disabilities, the location of injury was roads and main roads, the activity at the time of injury was daily life, the mechanism of injury was falling and the type of transportation accident was collision with passenger cars. In the old-old, the characteristics by type of injury were fractures, the intentionality of the injury was unintentional, the place of injury was residence, the activity at the time of injury was daily life, the mechanism of injury was falling and the type of transportation accident was pedestrian. In conclusion, the old-old were more likely to suffer injuries at home and in daily life than the young-old with disabilities, and old-old injured patients were more active than the old-old, resulting in higher falls and transportation accidents and older seniors were more likely to have pedestrian accidents. Based on the information collected from the young-old and old-old, it is believed that efforts to prevent damage that consider the characteristics of the elderly are necessary.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.58-68
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• 2007

Thorough preparations and investigations must be done in order to prevent the hazards which will happen when workers work on the roof of the rolling stocks. Some industrial disasters can be prevented by solving the risks involved in the working of the work processes. Falling accidents tend to happen from the roof of the trains when workers work in the high position. So through the analysis of the 4M( Man. Machine, Media, Management), this paper analyzed the risk factors among the roof works and all problems of the maintenance. Also this paper suggests new devices or new methods to prevent falling disasters and to relieve the mental senses of uneasiness.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.63-69
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• 2014

The large-sized, complex, and multi-storied construction industry caused the increasing construction amount together with insufficient skilled workers to increase the probability of occurring accident, resulting in the most construction accidents next to manufacturing industry. Death accidents have risen to serious level, compared construction industry with manufacturing industry along the numbers of workers. Due to the main feature of one-time industry receiving orders, open-air dispersed production activity and the long-complex production process, the continuous efforts to prevent and manage safety accidents were made but the results were of no effect. They didn't deeply analyze the falling accidents that consist of half death accidents in construction industry. This study has classified in detail Missteps, Slip, Trip, Unstability and the others on the basis of gait characteristics, occurrence types, frequency and intensity of death accidents. This study suggested the effective methods on the construction safety management according to the causes of falling accidents. This study will be expect to be used as the basic data in the procedure and the program of safety management.

• Journal of the Korea Institute of Building Construction
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• v.19 no.2
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• pp.185-192
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• 2019

Construction has been well known as the industry in which accidents occur more often than other industries. The efforts to eliminate the accidents at construction sites need to be continuously conducted because they tend to cause the social problems such as massive loss of life and property. According to the Korea Occupational Safety and Health Agency (KOSHA), 26,570 (29.3 percent) out of 90,656 workers in total industrial accidents have been occurred in the construction industry in 2016. Particularly, the falling victims are the largest number, which is about 8,699. This number is increasing due to the increase of the large scale, high-rise, and complex construction structures and the various construction methods. In reality, there is a lack of analysis on the risk factors of safety accidents and preventive measures. Therefore, in this study, we have selected risk factors by analyzing the accident cases at construction sites. Based on the results, we conducted a safety practitioner-focused survey and had an interview with safety managers. In analyzing the cases, we have categorized them into three groups such as upper, middle, and lower and compared their statistical results. This study are expected to provide safety management guidelines with workers and safety managers to prevent previously fall accidents at construction site.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.47-54
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• 2021

According to the Korea Occupational Safety and Health Agency's (KOSHA) report on industrial accident statistics over the past four years, the number of casualties at construction sites from 2017 to June 2020 was about 93,158 and the number of deaths was about 1,977, showing a high trend of safety accidents among the eight major occupational groups, with the construction industry ranking third in total and the death rate being the highest in total. Among them, the number of deaths caused by falls in the entire occupational category is about 1,267, the highest rate of deaths due to contact is about 522, which is a frequent safety accident among the top three accident types. This paper aims to help reduce the overall proportion of construction safety accidents by developing smart safety sensing devices using ultrasonic sensors to prevent two types of safety accidents, which have the highest rate of occurrence among various types of safety accidents occurring at construction sites.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.821-830
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• 2023

This research aims to identify risk factors for fall accidents at small and medium-sized construction sites through a comprehensive regression analysis. Initially, the study involved collecting a decade's worth of fall accident data from these sites. A t-test confirmed a significant variation in the treatment duration following fall accidents between two distinct groups: small and medium-sized versus large construction sites. Subsequently, a regression analysis was conducted to establish a model highlighting the risk factors associated with safety accidents. The factors influencing fall accidents were determined to be, in descending order of impact, the time of the accident, the day of the accident, and the occupational classification. The findings from this study are expected to serve as foundational data for enhancing policies and conducting statistical analyses tailored to construction site sizes. They also provide crucial information for future research on risk quantification at small and medium-sized construction sites.