• 동서간호학연구지
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• 제27권1호
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• pp.61-69
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• 2021

Purpose: This study aims to determine the association between fear of falling, falls efficacy, and fall prevention behaviors in older adults in outpatient and identify the factors affecting fall prevention behaviors. Methods: This was a descriptive study. A total of 137 older adults aged 60 or older with more than one disease in an outpatients setting were recruited from a university hospital in Seoul. Data were collected from March 11 through April 8, 2016. Results: The experience of a fall was significantly associated with age (t=-2.71, p=.008), gender (𝛘²=14.32, p<.001), education (𝛘²=6.05, p=.049), income (𝛘²=9.93, p=.019), physical activity status (𝛘²=16.84, p<.001), and the number of diseases (t=-2.89, p=.004). Fall experience was not significantly associated with marital status (𝛘²=3.79, p=.285). Multiple regression analysis revealed that the most influential factor of fall prevention behaviors was the fear of falls (β=-.37, p<.001), followed by the number of diseases (β=-.20, p=.013). Conclusion: It is necessary to provide preventive fall education with older adults in outpatients settings considering the fear level of falling and the numbers of accompanying diseases.

• 근관절건강학회지
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• 제31권2호
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• pp.65-74
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• 2024

Purpose: This study was conducted to verify fall predictive power and reasonable fall risk assessment tool by a comparative analysis of the sensitivity, specificity, positive forecast and negative forecast of each tool by applying Morse Fall Scale (MFS), Johns Hopkins Fall Risk Assessment Tool (JHFRAT), and Fall Assessment Scale-Korean version (FAS-K) through electronic medical records to adult patients hospitalized in a general hospital in Korea. Methods: We performed a retrospective evaluation study from January to December 2018, 123 fall groups experiencing falls during hospitalization and 123 non-falls groups were selected. Data presented a reasonable assessment tool that predicts and distinguishes fall high-risk patients through area comparison based on the ROC curve for each tool. Results: In the ROC curve analysis by fall risk assessment group, the AUC of MFS is shown to be .706 (good), JHFRAT is shown to be .649 (sufficient) and FAS-K is shown to be .804 (very good). FAS-K at a cut-off score of 4, sensitivity, specificity, and positive and negative prediction values were 83.7%, 60.2%, 67.8%, and 78.7%, respectively. Conclusion: Based on the above findings, it is believed that the FAS-K was presented as a suitable and reasonable tool for predicting falls for adult patients in general hospitals.

• 센서학회지
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• 제26권1호
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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.

• The Journal of Korean Physical Therapy
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• 제20권3호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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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 Multimedia Information System
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• 제7권4호
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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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• 제11권1호
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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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• 제12권4호
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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.

• 전기학회논문지
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• 제60권6호
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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.

• 지역사회간호학회지
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• 제27권4호
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• pp.380-387
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• 2016

Purpose: The purpose of this study was to examine the mediating and moderating effects of fall-related efficacy and social support on the relationship between frailty and health-related quality of life among rural community-dwelling elderly. Methods: A cross-sectional survey was conducted with a convenient sampling method, and data of 438 elderly residents living in a rural community was used. The structured questionnaire included items from the Euro Quality of life-5 Dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/ depression), fall-related efficacy and social support. Results: Analysis of the mediating effect of fall-related efficacy and social support showed that there was significant mediating influence of fall-related efficacy on the relationship between frailty and health-related quality of life. There were no moderating effects of fall-related efficacy and social support. Conclusion: The findings suggest that fall-related efficacy may play a role in reducing the effect of frailty on health-related quality of life and underscore the need to consider ways of enhancing fall-related efficacy in interventions for rural community-dwelling frail elderly.