• Physical Therapy Rehabilitation Science
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• v.12 no.1
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• pp.26-32
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• 2023

Objective: This study attempted to compare the effects of bridge exercise using a sling according to the angle of the ankle to confirm the effective lower extremity muscle activation posture of patients with patellofemoral pain syndrome(PFPS). Design: Cross-sectional study Methods: Seventeen patients with PFPS were recruited and the muscle activities of the vastus medialis, vastus lateralis, rectus femoris, and gluteus maximus were measured according to the ankle position (dorsiflexion, neutral, plantar flexion). After measuring the maximum number of isometric contractions of vastus medialis, vastus lateralis, rectus femoris, and gluteus maximus, bridging exercise using a sling according to each ankle posture was applied to measure lower extremity muscle activity. The evaluation was performed 3 times for 10 seconds. The three ankle postures were randomly performed and the average values were compared. Results: As a result of this study, the vastus medialis muscle showed high muscle activity in the order of dorsiflexion, plantar flexion, and neutral position bridge exercise (p<0.05). And the vastus lateralis showed high muscle activity in the order of dorsiflexion, neutral, and plantar flexion (p<0.05). However, rectus femoris and gluteus maximus did not show significant muscle activity according to the ankle posture, but muscle activity was highest in the dorsiflexion posture. Conclusions: As a result of this study, muscle activity was high in the order of vastus medialis and vastus lateralis during ankle dorsiflexion. This is thought to be a major factor that can be applied in various ways in clinical practice according to the ankle angle when treating PFPS patients.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.28 no.1
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• pp.71-80
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• 2022

Background: This study investigated the effect of robot-assisted arm training on muscle activity of arm and weight bearing in stroke patients. Methods: The study subjects were selected 20 stroke patients who met the selection criteria. 10 people in the robot-assisted arm training group and 10 people in the task-oriented arm training group were randomly assigned. The experimental group performed robot-assisted arm training, and the control group performed task-oriented arm training for 6 weeks, 5 days a week, 30 minutes a day. The measurement tools included surface electromyography and smart insole system. Data were analyzed using independent sample t-test and the paired sample t-test. Results: Comparing the muscle activity of arm within the group, the experimental group and the control group showed significant differences in muscle activity in the biceps brachii, triceps brachii, anterior deltoid, upper trapezius, middle trapezius, and lower trapezius. Comparing the muscle activity of arms between the groups, the experimental group showed significant difference in all muscle activity of arm compared to the control group. Comparing the weight bearing within the groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings and there were significant differences in anterior and posterior weight bearing. The control group showed significant difference only in the non-affected side weight bearing. Comparing the weight bearings between groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings compared to the control group. Conclusion: This study confirmed that robot-assisted arm training applied to stroke patients for 6 weeks significantly improved muscle activity of arm and weight bearing. Based on these results, it is considered that robot-assisted arm training can be a useful treatment in clinical practice to improve the kinematic variables in chronic stroke patients.

• Physical Therapy Korea
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• v.30 no.3
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• pp.194-201
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• 2023

Background: Falls are a common and serious problem in the elderly population. Muscle strength and balance are important factors in the prevention of falls. The Y-balance test (YBT) is used to assess dynamic postural control and shows excellent test-retest reliability. However, no studies have examined the relationship between lower-limb strength and YBT scores in elderly women. Objects: This study aimed to examine the relationship between lower-limb strength and YBT scores in elderly women. Methods: Thirty community-dwelling elderly women participated in the study. Lower-limb strength including hip flexor, hip extensor, hip abductor (HAB), hip adductor (HAD), knee flexor, knee extensor, ankle dorsiflexor, and ankle plantar flexor (PF) muscles was examined using a smart KEMA strength sensor (KOREATECH Inc.), and the YBT was used to assess dynamic balance. Relationship between lower-limb strength and YBT was demonstrated using a Pearson's correlation coefficient. Results: HAB strength (r = 0.388, p < 0.05), HAD strength (r = 0.362, p < 0.05), and ankle PF strength (r = 0.391, p < 0.05) positively correlated with the YBT-anterior direction distance. Ankle PF strength was positively correlated with the YBT-posteromedial direction distance (r = 0.396, p < 0.05) and composite score (r = 0.376, p < 0.05). Conclusion: The results of this study suggest that HAB, HAD, and ankle PF strengths should be considered for dynamic postural control in elderly women.

• Smart Structures and Systems
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• v.32 no.1
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• pp.1-7
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• 2023

In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

• Convergence Security Journal
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• v.22 no.2
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• pp.27-42
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• 2022

In the era of the Fourth Industrial Revolution, digital transformation to increase the effectiveness of industry is becoming more important to achieving the goal of industrial innovation. The digital new deal and smart defense are required for digital transformation and utilize artificial intelligence, big data analysis technology, and the Internet of Things. These changes can innovate the industrial fields of national defense, society, and health with new intelligent services by continuously expanding cyberspace. As a result, work productivity, efficiency, convenience, and industrial safety will be strengthened. However, the threat of cyber-attack will also continue to increase due to expansion of the new domain of digital transformation. This paper presents the risk scenarios of cyber-attack threats in the Fourth Industrial Revolution. Further, we propose a preemptive intrusion response model to bolster the complex security environment of the future, which is one of the fundamental alternatives to solving problems relating to cyber-attack. The proposed model can be used as prior research on cyber security strategy and technology development for preemptive response to cyber threats in the future society.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.1-7
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• 2023

The advent of deep learning technologies has led to the development of various medical applications, making healthcare services more convenient and effective. Among these applications, heart rate estimation is considered a vital method for assessing an individual's health. Traditional methods, such as photoplethysmography through smart watches, have been widely used but are invasive and require additional hardware. Recent advancements allow for contactless heart rate estimation through facial image analysis, providing a more hygienic and convenient approach. In this paper, we propose a lightweight methodology capable of accurately estimating heart rate in mobile environments, using a specialized 2-channel network structure based on 2D convolution. Our method considers both subtle facial movements and color changes resulting from blood flow and muscle contractions. The approach comprises two major components: an Encoder for analyzing image features and a regression layer for evaluating Blood Volume Pulse. By incorporating both features simultaneously our methodology delivers more accurate results even in computing environments with limited resources. The proposed approach is expected to offer a more efficient way to monitor heart rate without invasive technology, particularly well-suited for mobile devices.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.471-478
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• 2023

The purpose of this study is to examine the effects of parental abuse and neglect on adolescents' internet addiction, and to verify the mediating effect of subjective happiness in the relationship between parental abuse and neglects and adolescent internet addiction. To this end, dat from the 16th year of the 2021 Korea Welfare Panel(KWPS) conducted by the Korea Institute for Health and Social Affairs were used. In this study, 1st, 2nd and 3rd graders of high school were analyzed, and data from a total of 325 students were analyzed. The analysis utilized SPSS 27.0 and Hayes(2013)'s Macro Process(model 4) to verity correlation analysis and mediating effects between related variables. The results of the analysis are as follows: First, abuse and neglect from parents directly affect adolescents' Internet addiction. Second, it was analyzed that subjective happiness mediated the effect of parental abuse and neglect on adolescents' Internet addiction.