• 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.

• 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.

• 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 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.

• 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.

• Research in Community and Public Health Nursing
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• v.27 no.4
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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.

• Journal of Korean Academy of Nursing
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• v.44 no.5
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• pp.525-533
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• 2014

Purpose: This study was done to propose and test a predictive model that would explain and predict fall prevention behaviors in postmenopausal women. The health belief model was the theoretical basis to aid development of a nursing intervention fall prevention program. Methods: Data for 421 postmenopausal women were selected from an original data set using a survey design. The structural equation model was tested for 3 constructs: modifying factors, expectation factors, and threat factors. Expectation factors were measured as relative perceived benefit (perceived benefit minus perceived barrier), self-efficacy, and health motivation; threat factors, as perceived susceptibility (fear of falling) and perceived severity (avoiding activity for fear of falling); and modifying factors: level of education and knowledge about fall prevention. Data were analyzed using SPSS Windows and AMOS program. Results: Mean age was 55.7 years (range 45-64), and 19.7% had experienced a fall within the past year. Fall prevention behaviors were explained by expectation and threat factors indicating significant direct effects. Mediating effect of health beliefs was significant in the relationship between modifying factors and fall prevention behaviors. The proposed model explained 33% of the variance. Conclusion: Results indicate that fall prevention education should include knowledge, expectation, and threat factors based on health belief model.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.447-452
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• 2000

In this study, the design of Free-Fall Simulator was carried out using concept of vertical wind tunnel. Free-Fall Simulator is not an experimental equipment but a training equipment. Therefore Free-Fall Simulator needs a large training section compared with test section of wind tunnel and has critical limit of height. These limits bring about the difficulty of design for a return passage. Due to small area ratio, the downstream flow of training section with high speed is not decelerated adequately to the fan section. High-speed flow leads to great losses in the small area ratio diffuser and corner. So design of diffusers and corners located between training section and fan section has a great effect on the Free-Fall Simulator performance. This study used an estimation method of subsonic wind tunnel performance. It considered each section of Free-Fall Simulator as an independent section. Therefore loss of one section didn't affect loss of other sections. Because losses of corner with vane and $1^{st}$ diffuser are most parts of overall Free-Fall Simulator, this study focused on the design of these sections.