• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.303-310
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• 2016

Recently there has been growing popularity in exergame, such as Wii Sport or Xbox Fitness game, which enables users to get physical exercise while playing the games. In such experienced exercise games, the user's posture recognition is very important to find out exactly how much the users need to take their body posture as compared to the proper posture. This paper proposes a new exercise posture recognition model designed for the exercise game content for the elderly. The proposed model is based on extracting feature points of a skeleton model provided by the Kinect sensor to generate the feature vectors to recognize the user's exercise posture information. This paper describes the design and implementation of the exercise posture recognition model and demonstrates the feasibility of this proposed posture recognition model through a simple experiment. The experimental results showed 94.52% of average accordance rate for 12 exercise postures of 10 participants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2275-2280
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• 2009

u-Healthcare service is one of attractive applications in ubiquitous environment. In this paper, we propose a method to recognize exercises using a new accelerometer based body-attached platform for supporting u-Healthcare service. The platform consists of a device for measuring accelerometer data and a device for receiving the data. The former measures a user's motion data using a 3-axis accelerometer. The latter transmits the accelerometer data to a computer for recognizing the user's exercise. The algorithm for exercise recognition classifies the type of exercise using principle components analysis(PCA) from the accelerometer data transformed by discrete fourier transform(DFT), and estimates the repetition count of the recognized exercise using a peak detection algorithm. We evaluate the performance of the algorithm from the accuracy of the recognition of exercise type and the error rate of the estimation of repetition count. In our experimental result, the algorithm shows the accuracy about 98%.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.28 no.2
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• pp.15-24
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• 2022

Background: This study investigated the perception of community care-based tele exercise rehabilitation according to demographic characteristics of physical therapists and presented basic data for the spread of tele exercise rehabilitation within community care. Methods: The study collected and analyzed data from 195 physical therapists. The analysis was performed using frequency analysis with 10 general characteristics, 13 tele exercise rehabilitation recognition questions, and a total of 23 questions. Analysis of general characteristics of study subjects and recognition of tele exercise rehabilitation were expressed in terms of frequency and percentage using frequency analysis. Chi-squared test was used to compare general characteristics and tele exercise rehabilitation recognition. Correlation analysis of major sociodemographic variables affecting the perception of remote exercise rehabilitation was conducted. Results: The awareness level of physical therapists for remote exercise rehabilitation was confirmed. The difference in the recognition of remote motor rehabilitation in the number of therapists, career, hospital form according to the sociodemographic characteristics showed statistically significant differences. Conclusion: It is necessary to first raise awareness of therapists through the promotion of tele exercise rehabilitation, and furthermore, in the future, it will be necessary to find a policy direction and plan on how tele exercise rehabilitation can be applied to rehabilitation services in local communities care.

• Journal of Korean Public Health Nursing
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• v.25 no.2
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• pp.266-275
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• 2011

Purpose: The study investigated the relationship of health behavior with bone mineral density (BMD) for college students. Methods: A descriptive study was done using a convenient sampling of college students (n=116) enrolled in a health-related department. Height, weight, body mass index (BMI), health behaviors recognition of subjective health, smoking, drinking, exercise, food habits, intake of calcium, and family history of fracture were measured. BMD was measured at the right forearm using Hologic lunar duel-energy X-ray absorptiometry. Data were analyzed using descriptive analysis, t-test, ANOVA and multiple linear regression. Results: The prevalence of osteopenia and osteoporosis was 41.4% and 22.4%, respectively. Significant relationships were observed between BMD of right forearm and gender, BMI, subjective health recognition, drinking, and exercise (p<.05). Results of linear regression after adjusted BMD were increased concerning subjective health recognition and regular exercise (p<.05). Conclusions: Subjective health recognition and exercise carries positive effects on BMD. We recommend for college students that healthy behaviors like proper weight, smoking cessation, regular exercise, regular food habits, and health awareness are helpful to BMD.

• Korean Journal of Community Nutrition
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• v.12 no.3
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• pp.247-258
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• 2007

This study was conducted to investigate weight control status and associated factors among health-related major female college students. The subjects consisted of 41 nutrition and 78 physical exercise major female college students. Nutrient intakes, biochemical index, nutrition knowledge (recognition and accuracy), interest of weight control, body satisfaction, self-recognition of health condition, self-evaluated body weight were studied. About 73% of nutrition and 79% of physical exercise major female students were in the normal range of BMI ($18.5{\sim}23$) and 2% of nutrition and 1% of physical exercise major female students were underweight, 10% of nutrition and 6% of physical exercise major female students were obese. There were no significant differences in height and weight by major but %body fat and WHR in physical exercise majors were significantly lower than nutrition major students (respectively p<0.01, p<0.05). Overall, nutrition intakes of subjects were not shown to be appropriate, especially Ca/P of subjects was shown $0.54{\sim}0.64$, fat% out of energy of subjects was shown $24.7{\sim}29.0$ and Na intake was shown above 2000mg. Recognition and accuracy of nutrition were higher than those of physical exercise majors (p<0.001). There were no significant differences in self-recognition of health condition, self-evaluated body weight, satisfaction of body shape by major and weight control attempt. But interest of weight control of attempter was higher than that of no-attempter in nutrition (p<0.05) and physical exercise major students (p<0.01). Significantly negative correlation was found in satisfaction of body and BMI, body fat mass, %bodyfat, WHR. And significantly positive correlation was found in interest of weight control and BMI, %bodyfat, WHR. It was noticeable to see that interest of weight control was positively correlated to accuracy and accuracy was negatively correlated to blood cholesterol level. Therefore, proper nutrition education for female college students is needed in order to improve their weight control-related health.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.381-391
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• 2018

The goal of current study was to investigate the perception of emotional lighting according to the types of exercise participation of fitness center users. The subjects of this study were 292 people in Gyeongin area fitness center. As a survey tool, the color types used in the study of Oh & Kwak(2015) were used, and the color recognition was based on the emotional adjective scale used in Lee(1997). Data were analyzed using crossover, independent t-test and one-way ANOVA using SPSS23.0. The following conclusions can be drawn from the results of this study. First, emotional lighting color preference of fitness users varies according to Gender. Second, the difference of perception of color by emotional illumination of fitness users was found to be partially different in color and sex. Third, there was a difference in color recognition among emotional lighting color recognition according to exercise participation type of fitness users.

• 재활복지
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• v.20 no.3
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• pp.105-124
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• 2016

The This study aims to verify the causal relationship among physically disability persons participation in exercise, heart rate variance, and facial expression recognition. To achieve such research goal, this study targeted 139 physically disability persons and as for sampling, purposive sampling method was applied. After visiting a sporting stadium and club facilities that sporting events were held and explaining the purpose of the research in detail, only with those who agreed to participate in the research, their heart rate variance and facial emotion awareness were measured. With the results of measurement, mean value, standard deviation, correlation analysis, and structural equating model were analyzed, and the results are as follows. The quantity of exercise positively affected sympathetic activity and parasympathetic activity of autonomic nervous system. Exercise history of physically disability persons was found to have a positive influence on LF/HF, and it had a negative influence on parasympathetic activity. Sympathetic activity of physically disability persons turned out to have a positive effect on the recognition of the emotion, happiness, while the quantity of exercise had a negative influence on the recognition of the emotion, sadness. These findings were discussed and how those mechanisms that are relevant to the autonomic nervous system, facial expression recognition of physical disability persons.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.396-402
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• 2016

Recently, as the performance of smart phones has advanced and their distribution has increased, various functions in existing devices are accumulated. In particular, functions in smart devices have matured through improvement of diverse sensors. Various applications with the development of smart phones get fleshed out. As a result, services from applications promoting physical activity in users have gotten attention from the public. However, these services are about diet alone, and because these have no exercise motion recognition capability to detect movement in the correct position, the user has difficulty obtaining the benefits of exercise. In this paper, we develop exercise motion-recognition software that can sense the user's motion using a sensor built into a smart phone. In addition, we implement a system to offer exercise with friends who are connected via web server. The exercise motion recognition utilizes a Kalman filter algorithm to correct the user's motion data, and compared to data that exist in sampling, determines whether the user moves in the correct position by using a DTW algorithm.

• The Research Journal of the Costume Culture
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• v.25 no.4
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• pp.529-542
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• 2017

The purpose of this study is to utilize technology as basic data for smart clothing product research and development. This technology can recognize user's motion according to characteristics types and functions of wearable smart clothing products. In order to analyze the case of motion recognition products, we searched for previous research data and cases referred to as major keywords in leading search engines, Google and Naver. Among the searched cases, information on the characteristics and major functions of the 42 final products selected on the market are examined in detail. Motion recognition for smart clothing products is classified into four body types: head & face, body, arms & hands, and legs & feet. Smart clothing products was developed with various items, such as hats, glasses, bras, shirts, pants, bracelets, rings, socks, shoes, etc., It was divided into four functions health care type for prevention of injuries, health monitor, posture correction, sports type for heartbeat and exercise monitor, exercise coaching, posture correction, convenience for smart controller and security and entertainment type for pleasure. The function of the motion recognition smart clothing product discussed in this study will be a useful reference when designing a motion recognition smart clothing product that is blended with IT technology.

• 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.