AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning
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 noninv...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2608 |
| _version_ | 1827307153340235776 |
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| author | Jiwun Yoon Sang-Yong Lee Ji-Yong Lee |
| author_facet | Jiwun Yoon Sang-Yong Lee Ji-Yong Lee |
| author_sort | Jiwun Yoon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:34:25Z |
| format | Article |
| id | doaj.art-6050c2266c454579a419897ecc8f29f8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:34:25Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6050c2266c454579a419897ecc8f29f82024-03-27T13:20:13ZengMDPI AGApplied Sciences2076-34172024-03-01146260810.3390/app14062608AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer LearningJiwun Yoon0Sang-Yong Lee1Ji-Yong Lee2Center for Sports and Performance Analysis, Korea National Sport University, Seoul 05541, Republic of KoreaCenter for Sports and Performance Analysis, Korea National Sport University, Seoul 05541, Republic of KoreaCenter for Sports and Performance Analysis, Korea National Sport University, Seoul 05541, Republic of KoreaHumans 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.https://www.mdpi.com/2076-3417/14/6/2608somatotypeendomorphymesomorphyectomorphyMobileNetV23D body scanners |
| spellingShingle | Jiwun Yoon Sang-Yong Lee Ji-Yong Lee AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning Applied Sciences somatotype endomorphy mesomorphy ectomorphy MobileNetV2 3D body scanners |
| title | AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning |
| title_full | AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning |
| title_fullStr | AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning |
| title_full_unstemmed | AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning |
| title_short | AI Somatotype System Using 3D Body Images: Based on Deep-Learning and Transfer Learning |
| title_sort | ai somatotype system using 3d body images based on deep learning and transfer learning |
| topic | somatotype endomorphy mesomorphy ectomorphy MobileNetV2 3D body scanners |
| url | https://www.mdpi.com/2076-3417/14/6/2608 |
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