• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.15-20
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• 2023

In general, maintaining an incorrect sitting posture for a long time is widely known to adversely affect the spine. Recently, several researchers have been interested in the causal relationship between incorrect sitting posture and spinal diseases, and have been studying methods to precisely measure changes in sitting or standing posture to prevent spinal diseases. In previous studies, we have developed a sensor device capable of measuring real-time posture change, applied a momentum calculation algorithm to improve the accuracy of real-time posture change measurement, and verified the accuracy of the postural change measurement sensor. In this study, we developed a posture measurement and analysis device that considers changes in the center of body pressure through the developed sitting pressure measurement, and it confirmed the sensor as an auxiliary tool to increase the accuracy of posture correction training with improving the user's visual feedback.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.1
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• pp.177-186
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• 2017

Recently, Autonomous vehicles are getting hot attention. Amazon, the biggest online shopping service provider is developing a delivery system that uses drones. This kinds of platforms are need accurate attitude information for navigation. In this paper, a structure design of AHRS using low-cost inertia sensor is proposed. To estimate attitudes a Kalman filter which uses a quaternion based dynamic model, bias-removed measurements from MEMS Gyro, raw measurements from MEMS accelerometer and magnetometer, is designed. To remove bias from MEMS Gyro, an additional Kalman filter which uses raw Gyro measurements and attitude estimates, is designed. The performance of implemented AHRS is compared with high price off-the-shelf 3DM-GX3-25 AHRS from Microstrain. The Gyro bias was estimated within 0.0001[deg/s]. And from the estimated attitude, roll and pitch angle error is smaller than 0.2 and 0.3 degree. Yaw angle error is smaller than 6 degree.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.157-166
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• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.61-68
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• 2014

In the present study, we aimed to examine the effects of Schroth exercise(three-dimensional convergence exercise) on pulmonary function(vital capacity, and chest expansion), Cobb's angle, scoliometer angle, and erector spinae muscle activity. We examined 40 students with idiopathic scoliosis(Cobb's angle $10^{\circ}$). They were divided into the Schroth and conventional exercise groups, with 20 people in each group. Statistical analysis was performed by using SPSS 18.0 with a paired t-test(pre-post difference) and an independent t-test(between-group differnence). the result of were as follows; 1) After 8 weeks of Schroth exercise, significant improvements were observed in Cobb's angle, rib hump, vital capacity, chest expansion, and right thoracic longissimus muscle activity. 2) After 8 weeks of conventional exercise, a significant improvement was observed only in chest expansion. These findings indicate that the Schroth exercise program improved the Cobb's angle, rib hump, pulmonary function, and sEMG results. Therefore, this study demonstrates the usefulness of the Schroth exercise program for idiopathic scoliosis.

• The Journal of the Convergence on Culture Technology
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• v.1 no.2
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• pp.91-95
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• 2015

The purpose of this study was to evaluate the difference voice production, according to the pelvic inclination while in the sitting position. Measure the sound produced(pitch) in three positions with the Praat program. position: anterior tilt position, posterior tilt position, neutral position(seat surface tilted 15 degrees). We found that the mean values of pitch were statistically significant different according to three types of sitting positions (p<0.05). The following result was observed: anterior tilt position > posterior tilt position > neutral position. There was significant difference in the neutral position. This finding suggests that the seat surface inclinations have an effect on speech production. Especially, neutral position may be an effective posture that may help increases the speech production.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1163-1165
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• 2017

헬스케어와 IT가 결합한 융합산업이 발전하면서 웨어러블 디바이스와 스마트폰을 연계하여 운동정보를 제공하는 제품이 증가하고 있다. 특히 손목에 착용하는 스마트밴드를 통해 생체신호 및 움직임을 감지하고 신체상태 및 활동량을 제공하고 있다. 그러나 단순한 운동 동작의 횟수나 시간만을 알려주는 서비스를 제공하고 있어 정확한 운동 및 건강의 관리가 힘들다. 이에 본 논문에서는 사용자에게 운동의 정확성 여부를 알려주고 건강 및 운동에 대한 통계 정보를 제공하는 클라우드 서비스 기반의 운동 정보 제공 시스템을 제안한다. 제안 시스템은 스마트밴드와 모션 디바이스를 통해 모션 및 심박데이터를 취득하여 스마트폰을 통해 실시간으로 운동의 정확도 여부 및 활동 정보를 제공한다. 또한 클라우드 플랫폼의 서비스 가상 머신을 통해 세밀한 운동 자세 및 운동량, 신체 상태 통계 등의 피드백 서비스를 제공한다.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.51-57
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• 2016

The purpose of this study was to determine the effectiveness of the pelvic compression belt on the thickness of the erector spinae and multifidus during hip extension on quadruped position. Thirty male university students volunteered to participate in this study. The pelvic compression belt was positioned below the anterior superior iliac spines with the stabilizing pressure using elastic compression bands. Subjects were instructed to perform hip extension in quadruped position with and without applying the pelvic compression belt. The thickness of the erector spinae and multifidus was measured ultrasound during prone position, quadruped position without applying pelvic compression belt and quadruped position applying pelvic compression belt. Data were analyzed using repeated ANOVA. Muscle thickness of multifidus was significantly higher applying the pelvic compression belt than without applying the pelvic compression belt (p<.05). Muscle thickness of elector spinae was significantly higher applying the pelvic compression belt than without applying the pelvic compression belt (p<.05). Therefore, the research can contribute to the prescription and application of quadruped position exercises in clinical practices.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.181-190
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• 2021

Sports activities conducted by multiple people are difficult to proceed in a situation where a widespread epidemic such as COVID-19 is spreading, and this causes a lack of physical activity in modern people. This problem can be overcome by using online exercise contents, but it is difficult to check detailed postures such as during face-to-face exercise. In this study, we present a model that detects posture and tracks movement using IT system for better non-face-to-face exercise content management. The proposed motion tracking model defines a body model with reference to motion analysis methods widely used in physical education and defines posture and movement accordingly. Using the proposed model, it is possible to recognize and analyze movements used in exercise, know the number of specific movements in the exercise program, and detect whether or not the exercise program is performed. In order to verify the validity of the proposed model, we implemented motion tracking and exercise program tracking programs using Azure Kinect DK, a markerless motion capture device. If the proposed motion tracking model is improved and the performance of the motion capture system is improved, more detailed motion analysis is possible and the number of types of motions can be increased.