Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual’s body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

The purpose of this study was to classify sports match fixing based on the scope of human network and financial involvement. We analyze the concept of match fixing as defined by the International Olympic Committee and create a classification scheme based on four types of match fixing behavior: type 1—organization-based match fixing with money involved within an internal–external human network, type 2—relationship-based match fixing with the involvement of an internal–external human network without monetary involvement, type 3—sustenance-based match fixing with money involved within an internal human network, and type 4—goal-based match fixing with the involvement of an internal human network without monetary involvement. Lastly, we need to consider variables intrinsic and extrinsic to a sports game, which should be carefully examined and managed depending on the match fixing type to prevent match fixing behavior.

This study aimed to develop an English version of a doping drug-recognition system using deep learning-based optical character recognition (OCR) technology. A database of 336 banned substances was built based on the World Anti-Doping Agency’s International Standard Prohibited List and the Korean Pharmaceutical Information Center’s Drug Substance Information. For accuracy and validity analysis, 886 drug substance images, including 152 images of prescriptions and drug substance labels collected using data augmentation, were used. The developed hybrid system, based on the Tesseract OCR model, can be accessed by both a smartphone and website. A total of 5379 words were extracted, and the system showed character recognition errors regarding 91 words, showing high accuracy (98.3%). The system correctly classified all 624 images for acceptable substances, 218 images for banned substances, and incorrectly recognized 44 of the banned substances as acceptable. The validity analysis showed a high level of accuracy (0.95), sensitivity (1.00), and specificity (0.93), suggesting system validity. The system has the potential of allowing athletes who lack knowledge about doping to quickly and accurately check whether they are taking banned substances. It may also serve as an efficient option to support the development of a fair and healthy sports culture.

Humans share a similar body structure, but each individual possesses unique characteristics, which we define as one’s body type. Various classification methods have been devised to understand and assess these body types. Recent research has applied artificial intelligence technology utilizing noninvasive measurement tools, such as 3D body scanner, which minimize physical contact. The purpose of this study was to develop an artificial intelligence somatotype system capable of predicting the three body types proposed by Heath-Carter’s somatotype theory using 3D body images collected using a 3D body scanner. To classify body types, measurements were taken to determine the three somatotype components (endomorphy, mesomorphy, and ectomorphy). MobileNetV2 was utilized as the transfer learning model. The results of this study are as follows: first, the AI somatotype model showed good performance, with a training accuracy around 91% and a validation accuracy around 72%. The respective loss values were 0.26 for the training set and 0.69 for the validation set. Second, validation of the model’s performance using test data resulted in accurate predictions for 18 out of 21 new data points, with prediction errors occurring in three cases, indicating approximately 85% classification accuracy. This study provides foundational data for subsequent research aiming to predict 13 detailed body types across the three body types. Furthermore, it is hoped that the outcomes of this research can be applied in practical settings, enabling anyone with a smartphone camera to identify various body types based on captured images and predict obesity and diseases.

Background: To verify the hydration effects of oral rehydration solution (ORS) on athletes by comparing the degrees of fluid absorption and plasma volume changes following beverage consumption, including ORS. Methods: Thirty-one participants visited the testing laboratory 4 times at 1-week intervals to consume 1 L of beverage (e.g., water, ORS, and two sports drinks [SpD]) for 30 min on each visit. The urine output was measured 4 times at 1 h, 2 h, 3 h, and 4 h after beverage consumption. A blood sample was collected 3 times at 1 h, 2 h, and 3 h after beverage consumption. Body weight was measured once in 4 h after beverage consumption. Results: Body weight change was smaller for ORS than for water, SpD1, and SpD2 (p < 0.05). Cumulative urine output in 4 h was lower for ORS, SpD1, and SpD2 than for water (p < 0.05), and it was lower for ORS than for SpD2 (p < 0.05). BHI in 4 h was higher for ORS, SpD1, and SpD2 than for water (p < 0.05), and it was higher for ORS than for SpD2 (p < 0.05). There was no significant difference in PVC for different beverages at all test times, i.e.., 1 h, 2 h, and 3 h. Conclusions: We evaluated the hydration effects of the consumption of beverages, such as water, SpD, and ORS in athletes. ORS and SpD were more effective than water. A comparison between ORS and SpD showed that the result could vary depending on the type of SpD.

Background: This study was conducted to perform deep learning-based body shape cluster analysis using 3D Body Scanner. Methods: For this study, 54 variables were measured using 3D Body Scanner on 366 adult men and women at Korea National Sport University in 2022. Transformer learning and dimensionality reduction models were used to perform cluster analysis on the measured data. Mann-Whitney test and Kruskal-Wallis test were applied to compare the principal component differences of new scale characteristics, and all statistical significance levels were set at .05. Results: First, among the two methods for classifying body types, the transformer algorithm had a higher performance in body type classification. Second, in the classification of body type clusters, two clusters, endomorphic body type and ectomorphic body type, were divided into six clusters, two for cluster 1 and four for cluster 2. Conclusion: The six clusters provide more granular information than previous body type classifications, and we believe that they can be used as basic information for predicting health and disease.