• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.259-264
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• 2017

Birth weight of a new born baby provides very important information in evaluating many clinical issues such as fetal growth restriction. This paper analyzes birth weight data of babies born in Korea from 2011 to 2013, and it shows that there is a biologically implausible distribution of birth weights in the data. This implies that some errors may be generated in the data collection process. In particular, this paper analyzes the relationship between gestational period and birth weight, and it is shown that the birth weight data mostly of gestational periods from 28 to 32 weeks have noticeable errors. Therefore, this paper employs the finite Gaussian mixture model to classify the collected data points into two classes: non-corrupted and corrupted. After the classification the paper removes data points that have been predicted to be corrupted. This adjustment scheme provides more natural and medically plausible percentile values of birth weights for all the gestational periods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.91-98
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• 2022

Lifelog data continuously collected through a wearable device may contain many outliers, so in order to improve data quality, it is necessary to find and remove outliers. In general, since the number of outliers is less than the number of normal data, a class imbalance problem occurs. To solve this imbalance problem, we propose a method that applies Variational AutoEncoder to outliers. After preprocessing the outlier data with proposed method, it is verified through a number of machine learning models(classification). As a result of verification using body weight data, it was confirmed that the performance was improved in all classification models. Based on the experimental results, when analyzing lifelog body weight data, we propose to apply the LightGBM model with the best performance after preprocessing the data using the outlier processing method proposed in this study.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.393-395
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• 2012

많은 현대인들은 체중 관리를 위해 많은 시간과 노력을 쏟고 있으며 그중에서도 식단을 관리하는데 많은 힘을 기울이고 있다. 하지만, 전문지식이 없는 일반인이 자신이 먹은 식단을 분석하고 어떤 음식을 먹을지 계획하는 것은 쉽지 않다. 따라서 본고에서는 hypernetwork를 이용한 개인 맞춤형 식단 추천 알고리즘을 제안한다. 개발된 식단 추천 알고리즘은 사용자의 식단 로그 데이터를 기반으로 사용자의 식성에 맞고 적절한 칼로리를 지닌 식단을 구성하여 추천한다. 특히, 식품 정보 DB 이외에 다른 추가 정보가 필요하지 않으며, 개인의 작은 식단 로그 데이터만으로도 동작 가능한 장점을 가지고 있다. 본 연구실에서는 개발된 알고리즘을 이용하여 개인 체중 관리 어플리케이션인 DietAdvisor를 제작하였으며, 사용자는 어플리케이션을 통해 실제 식단 추천 및 그 외의 체중관리에 필요한 서비스를 제공받을 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1294-1296
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• 2017

현대 사회의 많은 사람은 수없이 많은 다이어트를 시도하고 그중 많은 사람은 다이어트에 흥미를 느끼지 못해 다이어트에 실패한다. 본 논문에서는 성공적인 다이어트를 위해 경쟁 심리를 이용한 자동 측정 체중 관리 시스템의 개발 내용에 관해 기술한다. 이 시스템은 사용자 구분이 가능한 체중계와 사용자 체중 데이터를 실시간 자동으로 받을 수 있는 애플리케이션, 그리고 모든 사용자의 체중 정보를 활용하여 경쟁 심리를 유발하기 위해 매칭이 가능한 서버로 구성된다. 본 시스템은 사용자들에게 적합한 매칭을 해주고, 서로 다이어트 방법과 식단을 공유할 수 있도록 함으로써 효과적인 다이어트를 할 수 있게 한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.101-110
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• 2023

This study was conducted to identify factors influencing weight loss efforts in hypertensive patients with obesity in Korea using data from the 2016-2019 National Health and Nutrition Examination Survey. Weight perception, weight loss efforts, and weight control methods were investigated for 1,910 subjects. Data were analyzed using descriptive analysis, cross-tabulation analysis and logistic regression. Among obese hypertensive patients, 12.6% perceived their weight as normal. Weight loss efforts were 2.03 times (95% CI: 1.48 to 2.78) higher in people with overweight perception than those with normal weight perception, and 1.74 times (95% CI: 1.33 to 2.26) higher in women than in men. In addition, those with class 1 obesity were 1.50 times (95% CI: 0.85 to 2.65) higher than those with class 3 obesity, and those with class 2 obesity were 2.16 times (95% CI: 1.16 to 4.00) higher than those with class 3 obesity. These results suggest that weight management approaches for hypertensive patients with obesity should be individually designed according to weight perception, gender, and obesity class.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.201-208
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• 2021

This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.377-385
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• 2009

Twenty five female students in Seoul participated and were divided into two group in the experiment of weight loss effect of two treatments. Fourteen students(Treatment A group), randomly chosen from the students, had fed on diet foods and exercised over 8 weeks, and the remaining students(Treatment B group) had fed on diet foods only for the same periods. Weights of 25 students had been measured repeatedly four times at an interval of two weeks during 8 weeks, It resulted from mixed model analysis of repeated measurements data that separate Toeplitz pattern for each treatment group was selected as the optimal covariance pattern. Based upon the optimal covariance pattern model, the baseline effect and time effect were found to be highly significant, but the treatment-time interaction effect was found to be insignificant. Finally, the students with diet foods and exercises were more effective in losing weight than the students with only diet foods were.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.337-346
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• 2019

The purpose of this study was to investigate the convergence factors affecting disease management efforts of the middle-aged population who have comorbidities of all three metabolic diseases: type 2 diabetes, hypertension, and dyslipidemia. This study used raw data from the 2015 community health survey(CHS). A multiple hierarchical regression analysis was performed that the included variables explained 20.1% of the variance in weight-loss efforts, 6.8% of exercise efforts and 5.3% diet efforts respectively. This study revealed associations among gender, socioeconomic status, and behavioral habits of smoking and drinking with disease-management efforts. It is important to design a health service or supportive intervention with consideration of multiple factors for patients with multiple metabolic disease.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.139-148
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• 2009

The present study was conducted for analyzing changes in physique of at menarche of athletic and non-athletes. The maximum growth age of height and weight during menarche was not different between non-athletes and the athletes. Second, among non-athletes, those who had menarche late were taller and heavier than those who did early, and among the athletes, those who had menarche late were taller but lighter. The development rate of height was higher and the development duration was longer in the athletes than in the non-athletes. The development rate of weight was similar between the non-athletes and the athletes, but the maximum rate was higher in the athletes.