Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression
Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which could provide acute and chronic pulmonary disease monitoring and diagnosis. This work presents a feasibility study for non-contact spirometry measurement...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/8e52b106ba8c4d2db1369a8d51592ddc |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:8e52b106ba8c4d2db1369a8d51592ddc |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:8e52b106ba8c4d2db1369a8d51592ddc2021-11-25T18:57:33ZNon-Contact Spirometry Using a Mobile Thermal Camera and AI Regression10.3390/s212275741424-8220https://doaj.org/article/8e52b106ba8c4d2db1369a8d51592ddc2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7574https://doaj.org/toc/1424-8220Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which could provide acute and chronic pulmonary disease monitoring and diagnosis. This work presents a feasibility study for non-contact spirometry measurements using a mobile thermal imaging system. Thermal images were acquired from 19 subjects for measuring the respiration rate and the volume of inhaled and exhaled air. A mobile application was built to measure the respiration rate and export the respiration signal to a personal computer. The mobile application acquired thermal video images at a rate of nine frames/second and the OpenCV library was used for localization of the area of interest (nose and mouth). Artificial intelligence regressors were used to predict the inhalation and exhalation air volume. Several regressors were tested and four of them showed excellent performance: random forest, adaptive boosting, gradient boosting, and decision trees. The latter showed the best regression results, with an R-square value of 0.9998 and a mean square error of 0.0023. The results of this study showed that non-contact spirometry based on a thermal imaging system is feasible and provides all the basic measurements that the conventional spirometers support.Luay FraiwanNatheer KhasawnehKhaldon LweesyMennatalla ElbalkiAmna AlmarzooqiNada Abu HamraMDPI AGarticlethermal cameranon-contact spirometryartificial intelligence regressionrespiration signalrespiration rate mobile applicationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7574, p 7574 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
thermal camera non-contact spirometry artificial intelligence regression respiration signal respiration rate mobile application Chemical technology TP1-1185 |
| spellingShingle |
thermal camera non-contact spirometry artificial intelligence regression respiration signal respiration rate mobile application Chemical technology TP1-1185 Luay Fraiwan Natheer Khasawneh Khaldon Lweesy Mennatalla Elbalki Amna Almarzooqi Nada Abu Hamra Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| description |
Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which could provide acute and chronic pulmonary disease monitoring and diagnosis. This work presents a feasibility study for non-contact spirometry measurements using a mobile thermal imaging system. Thermal images were acquired from 19 subjects for measuring the respiration rate and the volume of inhaled and exhaled air. A mobile application was built to measure the respiration rate and export the respiration signal to a personal computer. The mobile application acquired thermal video images at a rate of nine frames/second and the OpenCV library was used for localization of the area of interest (nose and mouth). Artificial intelligence regressors were used to predict the inhalation and exhalation air volume. Several regressors were tested and four of them showed excellent performance: random forest, adaptive boosting, gradient boosting, and decision trees. The latter showed the best regression results, with an R-square value of 0.9998 and a mean square error of 0.0023. The results of this study showed that non-contact spirometry based on a thermal imaging system is feasible and provides all the basic measurements that the conventional spirometers support. |
| format |
article |
| author |
Luay Fraiwan Natheer Khasawneh Khaldon Lweesy Mennatalla Elbalki Amna Almarzooqi Nada Abu Hamra |
| author_facet |
Luay Fraiwan Natheer Khasawneh Khaldon Lweesy Mennatalla Elbalki Amna Almarzooqi Nada Abu Hamra |
| author_sort |
Luay Fraiwan |
| title |
Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| title_short |
Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| title_full |
Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| title_fullStr |
Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| title_full_unstemmed |
Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression |
| title_sort |
non-contact spirometry using a mobile thermal camera and ai regression |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/8e52b106ba8c4d2db1369a8d51592ddc |
| work_keys_str_mv |
AT luayfraiwan noncontactspirometryusingamobilethermalcameraandairegression AT natheerkhasawneh noncontactspirometryusingamobilethermalcameraandairegression AT khaldonlweesy noncontactspirometryusingamobilethermalcameraandairegression AT mennatallaelbalki noncontactspirometryusingamobilethermalcameraandairegression AT amnaalmarzooqi noncontactspirometryusingamobilethermalcameraandairegression AT nadaabuhamra noncontactspirometryusingamobilethermalcameraandairegression |
| _version_ |
1718410475981504512 |