Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics
This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to an...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/1/129 |
| _version_ | 1797358192430153728 |
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| author | Luís Rodríguez-Cobo Luís Reyes-Gonzalez José Francisco Algorri Sara Díez-del-Valle Garzón Roberto García-García José Miguel López-Higuera Adolfo Cobo |
| author_facet | Luís Rodríguez-Cobo Luís Reyes-Gonzalez José Francisco Algorri Sara Díez-del-Valle Garzón Roberto García-García José Miguel López-Higuera Adolfo Cobo |
| author_sort | Luís Rodríguez-Cobo |
| collection | DOAJ |
| description | This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system’s effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection. |
| first_indexed | 2024-03-08T14:57:17Z |
| format | Article |
| id | doaj.art-54f1f4e2808d4195b94cc11a0fc36187 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:17Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-54f1f4e2808d4195b94cc11a0fc361872024-01-10T15:08:43ZengMDPI AGSensors1424-82202023-12-0124112910.3390/s24010129Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care DiagnosticsLuís Rodríguez-Cobo0Luís Reyes-Gonzalez1José Francisco Algorri2Sara Díez-del-Valle Garzón3Roberto García-García4José Miguel López-Higuera5Adolfo Cobo6CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, SpainPhotonics Engineering Group, University of Cantabria, 39005 Santander, SpainCIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, SpainAmbar Telecomunicaciones S.L., 39011 Santander, SpainAmbar Telecomunicaciones S.L., 39011 Santander, SpainCIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, SpainCIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, SpainThis work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system’s effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.https://www.mdpi.com/1424-8220/24/1/129thermalacousticsensorsremotelow-cost hardwareneural networks |
| spellingShingle | Luís Rodríguez-Cobo Luís Reyes-Gonzalez José Francisco Algorri Sara Díez-del-Valle Garzón Roberto García-García José Miguel López-Higuera Adolfo Cobo Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics Sensors thermal acoustic sensors remote low-cost hardware neural networks |
| title | Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics |
| title_full | Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics |
| title_fullStr | Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics |
| title_full_unstemmed | Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics |
| title_short | Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics |
| title_sort | non contact thermal and acoustic sensors with embedded artificial intelligence for point of care diagnostics |
| topic | thermal acoustic sensors remote low-cost hardware neural networks |
| url | https://www.mdpi.com/1424-8220/24/1/129 |
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