• 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.

• PNF and Movement
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• v.10 no.4
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• pp.9-23
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• 2012

Purpose : The purpose of this study was to investigate the effects of Proprioceptive Neuromuscular Facilitation(PNF) exercise on body functions(gait, balance, lower extremity power) of elderly women. Methods : This study applied PNF exercise in a fall prevention program in addition to fall prevention seminars to an experimental group of 15 subjects and applied only fall prevention seminars to a control group of 15 subjects. The PNF exercise consisted of three sessions per week for 8 weeks and fall prevention seminars were composed of three times educations(0th, 4th, and 8th week). As a result of statistical analyses, following conclusions were obtained. Results : A 8-week PNF exercise significantly improved gait function and balance functions, lower extremity functions of elderly women. Conclusion : The PNF exercise in a fall prevention program was found to be effective to improve body functions(gait, balance, lower extremity power) of elderly women. In other words, the PNF exercise needs to be considered as an effective intervention for elderly women in order to strengthen their body functions and in fall prevention program.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.67-73
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• 2016

According to industrial accident analysis of the Ministry of Employment and Labor in 2013, 38 workers were injured by fall accidents on average per day, and one worker died on average per day among them. In various industries such as heavy and construction industry, the full body harness is commonly used to prevent the fall accident. In developing full body harness, the designer considered only the workers' safety, without taking into account comfort and workability. The survey was conducted so as to search the problems of full body harness currently used by workers in heavy and construction industry. The survey questionnaire was given to 565 workers wearing full body harness in heavy and construction industry. The results of study showed that the development of new full body harness considering body size of korean was needed. The impotent factors for developing of new full body harness were the size and the weight of the full body harness. The full body harness taking into account body size was judged to contribute to more comfortable work, work efficiency and safety. The result of this study can be utilized as useful data in the development of new full body harness considering the body size of korean workers.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.17 no.2
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• pp.95-103
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• 2010

Purpose: To examine the effects of a fall prevention exercise program on the community-dwelling elderly. Methods: The nonequivalent control group pretest-posttest design involved 16. subjects in the experimental group and 17 in the control group. The experimental group received the fall prevention exercise program for 50 minutes, three times each week for 12 weeks. Results: After program participation, the experimental group of subjects showed significantly higher lower limb strength higher endurance, and higher balance than the control group of subjects. The danger of being injured in a fall was also significantly lower in the experimental group. However, there were no significant differences in body constituent factors, agility, and flexibility between the two groups after the intervention. Conclusion: The 12 week fall prevention exercise program was effective in increasing lower limb muscular strength, endurance, balance, and body strength, and in decreasing the danger status of fall injuries. These results suggest that this fall prevention exercise program could be utilized as an effective nursing intervention modality in elderly persons.

• Journal of the Korean Home Economics Association
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• v.30 no.4
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• pp.15-26
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• 1992

To investigate the seasonal effects on physiological responses of human body, clothing micro-climate, and subjective sensation, selected the cloths the most frequently dressed by men in spring and fall, and completed wearing trials in the climatic chamber. The results are as follows: 1. Rectal temp. ranged 36.8-37.1$^{\circ}C$ in either spring or fall, and no seasonal effect was found. 2. In skin temp., there was no seasonal effect in forehead, abdomen, and forearm. Skin temp. of chest was higher in spring than in fall. On the contrary, reverse was true in high and leg. Average skin temp. ranged 32.2-33.2$^{\circ}C$ in spring and 32.9-34.$0^{\circ}C$ in fall. 3. Average total sweat rate of spring, 79.4g/hr, was smaller than that of fall, 110.9g/hr. 4. Clothing temp. ranged 28.1-32.8$^{\circ}C$ in spring and 27.6-31.$0^{\circ}C$ in fall. Clothing humidity ranged 36.9-48.9% in spring and 38.2-51.1% in fall. Therefore, clothing microclimate was higher during fall than during spring. As results, skin temp. of the body core except chest did not show seasonal variation, but there was obvious seasonal variation in skin temp. of the extremities. Therefore, seasonal variation should be take into consideration in the experiments related to the cloth. In addition, standard for each season and the degree of work performance should be re-established in clothing micro-climate.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.533-539
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• 2015

The purpose of this study was to investigate the effects of 12weeks complexed lower body muscle-strengthening exercise program on fall risk in elderly women. Twenty subjects volunteered to participate who use a welfare center in W city For the study we divided into two groups: exercise group (EC, n=10, mean age:$69.6{\pm}2.2$), comparative group(CG, n=10, mean age:$71.3{\pm}4.6$). In order to investigate the effect of lower body muscle-strengthening program on the degree of risk of fall. It reached a conclusion as follows after having applied BBS (Berg Balance Scale) and OLST (One-Leg Stance Test) to examine the degree of risk of fall. As a result of changes in BBS and OLST, there were significant differences between EG and CG for each test(p<.00). Therefore, it confirmed that the application of complexed lower body muscle-strengthening program to the elderly who have a high risk of fall influences the risk of fall positively.

• 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.

• 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 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.