• The Journal of Korean Physical Therapy
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• v.20 no.3
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• pp.19-28
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• 2008

Purpose: We analyzed the incidence of falls and the related factors, circumstances, and consequences associated with falls in stroke patients. Methods: We recruited 127 stroke patients and used a self-reported questionnaire to measure fall prevalence rates and the related factors, circumstances, and consequences of falls. The chi-square test was used to establish associations between related factors. Results: The prevalence of falls in stroke patients was 69.3%, and was associated with gender and time since the stroke. Falls occurred 2-5 times (55.4%) poststroke and most subjects first fall in the 2$\sim$6 month (46.5%) after the stroke. Most (55%) falls occurred at the hospital. Walking was the most frequent circumstance for falls (38.5%). Most (54.4%) falls led to consequences such as fractures, ligament strains, bruises, or abrasions. Conclusion: Fall-prevention strategies decrease the number of falls and the severity of fall-related injuries. These data support the concept of preventive strategies for falls in stroke patients who are at risk.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.7-14
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• 2017

A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

• Journal of Multimedia Information System
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• v.7 no.4
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• pp.257-262
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• 2020

In this paper, a method of detecting the emergency situations such as body fall is proposed by using color images. We detect body areas and key parts of a body through a pre-learned Mask R-CNN in the images captured by a camera. Then we find the centerline of the body through the joint points of both shoulders and feet. Also, we calculate an angle to the center line and then calculate the amount of change in the angle per hour. If the angle change is more than a certain value, then it is decided as a suspected fall. Also, if the suspected fall state persists for more than a certain frame, then it is determined as a fall situation. Simulation results show that the proposed method can detect body fall situation accurately.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.370-375
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• 2023

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1751-1768
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• 2011

Falls are one of the most concerned accidents for elderly people and often result in serious physical and psychological consequences. Many researchers have studied fall detection techniques in various domain, however none released to a commercial product satisfying user requirements. We present a systematic modeling and evaluating procedure for best classification performance and then do experiments for comparing the performance of six procedures to get a statistical classifier based wrist-type fall detector to prevent dangerous consequences from falls. Even though the wrist may be the most difficult measurement location on the body to discern a fall event, the proposed feature deduction process and fall classification procedures shows positive results by using data sets of fall and general activity as two classes.

• Journal of the Korean Society of Safety
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• v.34 no.5
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• pp.132-138
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• 2019

Fall accident is the most frequent accident type of occupational accidents. As the age of workers increases, trip and fall accident increases more than other types of occupational accident in the middle-aged group. In this study, the gait characteristics of 25 middle-aged participants (mean ages 47.4, S.D. 5.8) were studied to analyze the trip and fall risks. The Minimum toe clearance(MTC) against the floor surface was measured in the variable conditions of gait speed by a motion capture system. In the 50s age group, the MTC decreased and the MTC tended to reduce the variation with increasing walking speed in the level walking. Therefore, the trip and fall risks for the 50s age group is higher than the 40s age group. Especially, the faster walking speed will increase the trip and fall risks even more.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1204-1208
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• 2011

In this study, we proposed the system that calculates the algorithm with an accelerometer signal and detects the fall shock and it's direction. In order to gather the activity patterns of fall status and attach on the subject's body without consciousness, the device needs to be small. With this aim, it is attached on the right side of subject's waist. With roll and pitch angle which represent the activity of upper body, the fall situation is determined and classified into the posture pattern. The impact is calculated by the vector magnitude of accelerometer signal. And in the case of the elderly keep the same posture after fall, it can distinguish the situation whether they can stand by themselves or not. Our experimental results showed that 95% successful detection rate of fall activity with 10 subjects. For further improvement of our system, it is necessary to include tasks-oriented classifying algorithm to diverse fall conditions.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.96-101
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• 2022

The Occupational Safety and Health Act holds that industrial safety helmets can be used as protective equipment to prevent the risk of injury in fall accidents. To better understand the importance given to PPE for the head, we analyzed the relevant regulations and guidelines in developed countries and reviewed the guidelines on testing safety helmets. The PPE regulations in Korea were notably different from those in other countries. First, except in Korea and Japan, safety helmets were used for protection against falling objects, flying objects, impact, or electric shock. However, the regulations did not recognize safety helmets as a PPE against fall hazards. Second, the impact energy applied on the helmet was within the range 50-100 J, and the helmet could protect only the upper part of the head against hazards such as the impact of falling objects, flying objects, etc. Third, in Korean regulations, the term "fall" was used in relation to the parts where the safety helmet was specified as a fall hazard PPE, unlike in other countries. We propose that the term "fall" should be revised to "shock" in Korean regulations for the safety helmet.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.

• The Korean Journal of Rehabilitation Nursing
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• v.16 no.1
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• pp.55-62
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• 2013

Purpose: The purpose of this study was to identify the factors affected with nurses' prevention activity against hospital fall. Methods: The data were collected from 325 nurses at C University Hospital in G City by using the structured questionnaires from February 21, 2011 to March 12, 2011. The data were analyzed by stepwise multiple regression. Results: The main factor associated with prevention activity against hospital fall was the attitude towards hospital fall (${\beta}$=.26, p<.001), the next one was the educational level (${\beta}$=.16, p=.002), and the last one was the frequency of fall prevention education (${\beta}$=.14, p=.009). The all factors could explain 11.1% of the variance in the nurses' prevention activities against hospital fall. Conclusion: Hospital managers need to make hospital culture to enhance the nurse's positive attitude about hospital fall prevention. In addition, educators need to develop educational programs including hospital fall prevention through academic curriculum and continuing education.