• Journal of KIISE
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• v.42 no.3
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• pp.281-288
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• 2015

A volcano disaster damage prediction system supports decision making for counteracting volcanic disasters by simulating meteorological condition and volcanic eruptions. In this system, a program called Fall3D generates predicted results for the diffusion of ash after a volcanic eruption on the basis of meteorological information. The relevant meteorological information is generated by a weather numerical prediction model known as Weather Research & Forecasting (WRF). In order to reduce the entire processing time without modifying these two simulation programs, pipelining can be used by partly executing Fall3D whenever the hourly (partial) results of WRF are generated. To reduce the processing time, successor programs such as Fall3D require that certain features be suspended until the part of the results that is based on prior calculation is generated by a predecessor. Even though Fall3D does not have a suspend or resume feature, pipelining effect can be produced by using the program's restart feature, which resumes simulation from the previous session. In this study, we suggest a workflow that can control the execution type.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.45-54
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• 2018

This paper presents a vision-based fall detection system to automatically monitor and detect people's fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.

• Journal of Fashion Business
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• v.26 no.2
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• pp.143-155
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• 2022

The purpose of this study was to develop protective clothing that could alleviate fall impacts. Fall impact protection pants for elderly women were designed, and motion adaptable hip pads and knee pads printed by 3D printing were integrated into the pants and evaluated. First, the design of the fall impact protection pants with variable motion was semi-loose fitting pants that could be worn and detached from the protective pad. A pad pocket was made in the lining inside the pants so that the protective pad could be fixed to the protective area. Second, in the evaluation of the appearance of the fall impact protection pants, the wearer group had a good score of 4.60 or higher for all questions on color, material, ease, and fit. In the evaluation of the insertion method of the protective pad, the flexibility of the pad, and the weight of the pad, the subjects' scores were 4.30~4.80. The fit of the fall impact protection pants was excellent in the texture and elasticity of the outside and inside of the pants. There was no discomfort due to the pad(4.60), and no difficulty in movement during wearing activities was reported. During squatting, it was evaluated as 4.80, indicating that the motion adaptable hip joint and knee pads were highly effective during operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.

• Journal of Industrial Convergence
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• v.16 no.3
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• pp.1-7
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• 2018

This study is a descriptive research study for examining the general characteristics and health-related characteristics of the elderly in a local community, investigating factors for the fear of fall and identifying the relationship between the fear of fall, attitude to fall and fall efficacy. The subject of this study was 140 senior citizens who are using a community center in D city. The collected data was analyzed with the SPSS/WIN 21.0 program. The result of the study showed there was a significant difference in the fear of fall according to gender, person living together, subjective health condition, pain and unsafe gait. There was a significant negative correlation between the fear of fall and fall fear and fall efficacy. However, fall fear and fall efficacy were positive correlated. This study aims to provide necessary preliminary data for developing fall prevention program that can improve the fear of fall, attitude to fall and fall efficay based on the results of this study.

• Journal of the Korean Society of Physical Medicine
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• v.11 no.3
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• pp.73-80
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• 2016

PURPOSE: The purpose of this study was to investigate the quality of life related to fall experiences in chronic stroke patients. METHODS: This cross-sectional study included 117 patients with stroke from 3 hospitals in D metropolitan city. General characteristics, including fall experiences and quality of life, were assessed through a face-to-face interviews conducted in a quiet place using a questionnaire. Measurement of quality of life in stroke patients was conducted using the Korean Stroke Specific Quality of Life Scale (SS-QOL). To identify the SS-QOL items related to fall experiences, the items of the SS-QOL were considered as independent variables, and the variables that were significantly different according to fall experiences were identified using a univariate analysis. A binary logistic regression was then performed using fall experiences as the independent variable. RESULTS: According to the univariate analysis, self help activities, social role, and upper extremity function were significantly lower in the fall group than that in the non-fall group (p<.05). The findings of the binary logistic regression confirmed that social roles and upper extremity function were the SS-QOL items that were related to fall experience in chronic stroke patients. CONCLUSION: These findings suggest that social roles and upper extremity function may be risk factors for fall experience in patients with chronic stroke.

• Bulletin of the Korean Chemical Society
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• v.3 no.3
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• pp.104-109
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• 1982

A method to calculate the magnetic moments for $d^1$ and $d^2$ complexes in a strong crystal field of trigonal symmetry has been developed in this work choosing the trigonal axis (Ⅲ) as the quantization axis. The calculated magnetic moments using this method for $d^1$ and $d^2$ complexes in a strong trigonal ligand field fall in the range of the experimental values. The dipolar shifts for $d^1$ and $d^2$ complexes in a strong trigonal ligand field are also calculated using the calculated magnetic susceptibility components. The calculated values of the dipolar shifts also fall in the reasonable range.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.5
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• pp.939-949
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• 2017

This study uses 3D printing technology to design and fabricate a fall impact protection pad with a spacer fabric structure. The design of the pads consists of hexagonal three-dimensional units connected in a honey-comb shape; in addition, the unit consists of a surface layer and a spacer layer. Protect pads were designed as either a hexagonal type or diamond type according to the surface layer structure; subsequently, a spacer filament was also designed as the most basic I-shape type. Designed pads were printed using four types of flexible filaments to select suitable material for a fall impact protection pad. Impact protection performance and bending stiffness were evaluated for the eight type of pad outputs. As a result of the impact protection performance evaluation, when the force of 6,500N was applied, the force passed through the pad was in the range of 1,370-2,132N. FlexSolid$^{(R)}$ and Skinflex$^{TM}$ showed good protection performance and cubicon flexible filament showed the lowest protection. NinjaFlex$^{(R)}$ was found to be the most flexible in the bending stiffness evaluation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2451-2454
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• 2005

In recent years, the importance of the characterization of fall for a fracture prevention system keeps increasing since fracture from a fall can lead to serious health problems. Fall is one of the major sources which increase morbidity in elderly people. In terms of the cost and the influence to the quality of life, the most serious injury with hip fractures is caused by falls. The traditional methods in characterizing fall patterns have been mainly by the epidemiological surveys. With surveys, the exact data of fall patterns can not been acquired. In this paper, we measured and analyzed with the parameters related to fall pattern such as velocities and accelerations during the motion of falls using 3D motion capture program. We acquired the parameters of the fall pattern of intentional and unexpected fall. The result showed that the variation of velocity and acceleration during fall was very important in characterizing fall pattern, which of vital importance for the development of a fracture prevention system and for the safety of the elderly.