Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology
In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/22/8844 |
| _version_ | 1797463934435852288 |
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| author | Ling-Cheng Hsu Shiang Hsu Tan-Hsu Tan Chia-Hsing Cheng Cheng-Chun Chang |
| author_facet | Ling-Cheng Hsu Shiang Hsu Tan-Hsu Tan Chia-Hsing Cheng Cheng-Chun Chang |
| author_sort | Ling-Cheng Hsu |
| collection | DOAJ |
| description | In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development. |
| first_indexed | 2024-03-09T17:59:45Z |
| format | Article |
| id | doaj.art-13460b92318e436a998f19d11c0e579e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:59:45Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-13460b92318e436a998f19d11c0e579e2023-11-24T09:56:42ZengMDPI AGSensors1424-82202022-11-012222884410.3390/s22228844Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network TechnologyLing-Cheng Hsu0Shiang Hsu1Tan-Hsu Tan2Chia-Hsing Cheng3Cheng-Chun Chang4Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering, National Formosa University, Yunlin 632301, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, TaiwanIn recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development.https://www.mdpi.com/1424-8220/22/22/8844low-cost miniature skin spectrum measurement systemdeep neural networkminiature spectrum sensing chip |
| spellingShingle | Ling-Cheng Hsu Shiang Hsu Tan-Hsu Tan Chia-Hsing Cheng Cheng-Chun Chang Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology Sensors low-cost miniature skin spectrum measurement system deep neural network miniature spectrum sensing chip |
| title | Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology |
| title_full | Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology |
| title_fullStr | Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology |
| title_full_unstemmed | Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology |
| title_short | Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology |
| title_sort | developing low cost mobile device and apps for accurate skin spectrum measurement via low cost spectrum sensors and deep neural network technology |
| topic | low-cost miniature skin spectrum measurement system deep neural network miniature spectrum sensing chip |
| url | https://www.mdpi.com/1424-8220/22/22/8844 |
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