• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.63-73
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• 2017

In this paper, we propose algorithm for determining the static and dynamic posture using the acceleration sensor of smartphone. The measured acceleration values are then analyzed according to a preprocessing to the respective axis (X, Y, Z) and posture (standing, sitting, lying) presents static posture determination criterion. The proposed static posture determination condition is used for static posture determination and dynamic posture determination. The dynamic posture is determined by using regression linear equations. In addition, transition state can be grasped by SVM change in dynamic posture determination. Experimental results are presented using data and app. Experiments were performed using data collected from 10 adults.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.11
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• pp.457-464
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• 2019

The weight of pig is one of the main factors in determining the health and growth state of pigs, their shipment, the breeding environment, and the ration of feed, and thus measuring the pig's weight is an important issue in productivity perspective. In order to estimate the pig's weight by using the number of pig's pixels from images, acquired from a Top-view camera, the posture determining and the head removal from images are necessary to measure the accurate number of pixels. In this research, we propose the fast and accurate method to determine the pig's posture by using a fast image processing technique, find the head location by using a fast deep learning technique, and remove pig's head by using light weighted image processing technique. First, we determine the pig's posture by comparing the length from the center of the pig's body to the outline of the pig in the binary image. Then, we train the location of pig's head, body, and hip in images using YOLO(one of the fast deep learning based object detector), and then we obtain the location of pig's head and remove an outside area of head by using head location. Finally, we find the boundary of head and body by using Convex-hull, and we remove pig's head. In the Experiment result, we confirmed that the pig's posture was determined with an accuracy of 0.98 and a processing speed of 250.00fps, and the pig's head was removed with an accuracy of 0.96 and a processing speed of 48.97fps.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.33-41
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• 2018

This study was designed to provide basic data for developing exercise program that helps correcting posture by knowing the effect of strengthening and elongation exercises of upper extremity muscle to forward head posture correction. In this study determined subjects whether they had forward head posture or not. On the basis of the New York state posture rating, if a subject's posture is match up with the normal standard posture, gives 5 points and if the posture is slightly get out of the normal standard posture, gives 3 points and if the posture is apparently get out of the standard, gives 1 points. When determining the forward head posture, if talus, humerus and outer ear center are on the same line, it is determined as normal and if outer ear center is off the line less than 1.0cm, it is a slight deformation and if outer ear center is off the line more than 1.0cm, it is a high deformation. In the study selected people who have more than 1 cm gap between two vertical lines start from outer ear center and acromion separately as subjects. Length between the ideal alignment line measured by using goniometer and temporal region showed statistically significant decrease as $2.36{\pm}1.07cm$ before the intervention and $1.06{\pm}0.88cm$ after the intervention. After 4 weeks of neck and chest extensor muscle exercise, the group who exercised both showed increase in range of neck joint motion and neck flexion of the forward head posture. Meanwhile the group who only exercised neck extensor muscle only and the group who only exercised chest extensor muscle didn't showed statistically significant result. That only the group who exercised both muscles showed significant result is the different with studies before. Because this study didn't target patient who had a lesion, couldn't compare effect of the conservative manner and exercise. However, this study provides the fact that the group who exercised both neck and chest muscle had more effect than the control group.

• Journal of computational fluids engineering
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• v.21 no.3
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• pp.71-76
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• 2016

In speed skiing, aerodynamic forces play an important role in determining performance of the skier. To predict aerodynamic effects of the posture of the skier on alpine downhill skiing, we constructed equation of motion of the skier and performed the corresponding CFD simulations. Comparing drag and lift of three different skier postures, it has been shown that drag decreases significantly by tucking upper body to lower body and stretching arms forward. Also, aerodynamic lift which worked as downforce in standing posture worked upward in tuck posture, reducing friction force between snow and ski. This indicates that tuck posture have advantages over standing posture in dual mechanism, namely by reducing drag and also increasing lift. By this two-dimensional initial study we could reveal the general tendency of the aerodynamic force over the skier's body. This study not only provides a theoretical foundation for the athletes to understand the aerodynamic effects of skier postures but also shed a light on towards more accurate and rational three-dimensional CFD simulation of skiers in the near future study.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.155-163
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• 2021

In this paper, we propose a four 2-link robotic leg landing system that is used for leveling the bottom of the landing system, even when the quadcopter posture is changed. The case of conventional skid type landing gear has a risk when the quadcopter lands on a moving vehicle because the skid type landing gear is tilted to the landing site at this situation. To solve this problem, it is necessary to level the bottom of the landing system when the quadcopter posture is changed in the flight. Therefore, the proposed landing system used a four 2-link robotic leg with leveling method. The leveling method was derived from the method of determining a plane. The superiority of the proposed system was verified with 6-axis stewart platform and real flight experiment, and it shows feasibility of leveling method and proposed landing system.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.781-790
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• 2024

As modern people's interest in exercise and health increases, the demand for exercise-related information and devices is increasing, and exercising in the wrong posture can lead to body imbalance and injury. Therefore, in this study, the purpose of this study is to correct the posture for health promotion and injury prevention through the correct exercise posture of users. It was developed using Arduino Uno R3, a pressure sensor, and an acceleration sensor as the main memory device of the system. The pressure sensor was used to determine the squat posture, and the acceleration sensor was used to determine three types of gait: normal step, nasolabial step, and saddle step. Data is transmitted to a smartphone through a Bluetooth module and displayed on an app to guide the user in the correct exercise posture. The gait was determined based on the 20˚ angle at which the foot was opened, and the correct squat posture was compared with the ratio of the pressure sensor values of the forefoot and hindfoot based on the data of the skilled person. Therefore, based on an experiment with about 90% accuracy when determining gait and 95% accuracy based on a 7:3 ratio of pressure sensor values in squat posture, a system was established to guide users to exercise in the correct posture by checking in real time through a smartphone application and correcting exercise in the wrong posture.

• Korean Journal of Occupational Health Nursing
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• v.11 no.2
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• pp.121-131
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• 2002

Objective: The purpose of this study was to determine the physical load by identifying harmfully working postures and to develop recommendations for improving the existing situation with nurses in ICU, thereby to provide the basis for development of work-related musculoskeletal preventive program. Method: Various types of tasks were recorded with a video camera to chart and analyze different postures by OWAS(Ovako Working Posture Analysing System). Collected data showed that poor postures were adopted, not only for lifting or repositoning a patient, but also for other tasks. Data Analysis: The performed activities were then divided into Nursing Intervention Classification. Altogether 128 postures were selected for analysis. Then they were classified into different OAC (OWAS Action Categories). From all the observation, unhealthy postures, for which corrective measures had to be considered immediately (i.e., 75% classified as OACII+III+IV) were found. Collected data were analyzed in terms of percentage, 2-tail Mann-Whitney U test. Result: Poor postures mainly occur during 'positioning the patient' and 'airway suctioning' in NIC. No difference was found (p=0.060) between the percentage of harmful posture adopted during the patient handling tasks and non-patient handling tasks. Conclusion: This study shows, that in the nursing profession with ICU not only occur during patient handling, but also during other activities. The OWAS method was useful in determining the physical load by locating potential activities due to harmfully working postures, providing a detailed description with analysis, and suggesting successful means to reduce postural load.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.603-610
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• 2015

This paper presents a multimodal sensor system that can determine the location of house space by analyzing the postures and heights of the residents. It consists of two sensors: a tilt sensor and an altimeter sensor. The tilt sensor measures the static and dynamic postures of the residents, and the altimeter sensor measures their heights. The sensor system includes a Bluetooth transmitter, and the server receives the measured data and determines the location in the house. We describe the process determining the locations of the residents after analyzing their postures and behaviors from the measured data. We also demonstrate the usefulness of the proposed system by applying it to a real environment.

• Physical Therapy Korea
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• v.19 no.1
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• pp.76-85
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• 2012

The purpose of this study was to identify the factors determining the participation restriction of chronic stroke patients based on international classification of functioning, disability, and health (ICF) model. Sixty-eight stroke patients participated. The participants were assessed participation restriction using the Korean version of London handicap scale (K-LHS), modified Barthel index (K-MBI) to measure activities of daily living, Berg balance scale (K-BBS) to assess balance, and the center for epidemiologic studies depression (K-CES-D) to gauge depression. Also, 3 minutes walking test (3MWT), gait velocity, asymmetric posture, and family support were assessed. A stepwise multiple regression analysis was used to explore the factors determining participation restriction. There were no significant different in the K-LHS and K-MBI results by gender (p>.05). Correlations between the K-LHS and K-MBI (r=-.656), K-BBS (r=-.543), K-CES-D (r=.266), 3MWT (r=-.363), gait velocity (r=.348), and family support (r=-.389) were significant (p<.05). Also, the K-MBI and family support were the factors that determined participation restriction (p<.05) and that 40.2% of the variation in the K-LHS can be explained. Therefore, it is suggested that evaluation and intervention of patient's activity level and extent of family support is necessary to reduce participation restriction of chronic stroke patients.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1100-1108
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• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.