Remote sensing technology: Difference between revisions
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==Medical imaging== | ==Medical imaging== | ||
Transdermal optical imaging, using video from a smartphone camera and using advanced machine learning may<ref name="pmid31382766">{{cite journal| author=Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ | display-authors=etal| title=Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. | journal=Circ Cardiovasc Imaging | year= 2019 | volume= 12 | issue= 8 | pages= e008857 | pmid=31382766 | doi=10.1161/CIRCIMAGING.119.008857 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=31382766 }} </ref> or may not<ref name="pmid29632098">{{cite journal| author=Raichle CJ, Eckstein J, Lapaire O, Leonardi L, Brasier N, Vischer AS | display-authors=etal| title=Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement). | journal=Hypertension | year= 2018 | volume= 71 | issue= 6 | pages= 1164-1169 | pmid=29632098 | doi=10.1161/HYPERTENSIONAHA.117.10647 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29632098 }} </ref> be able to determine a subject's blood pressure. | Transdermal optical imaging, using video from a smartphone camera and using advanced machine learning may<ref name="pmid31382766">{{cite journal| author=Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ | display-authors=etal| title=Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. | journal=Circ Cardiovasc Imaging | year= 2019 | volume= 12 | issue= 8 | pages= e008857 | pmid=31382766 | doi=10.1161/CIRCIMAGING.119.008857 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=31382766 }} </ref><ref name="pmid29865289">{{cite journal| author=Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR| title=Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. | journal=Sensors (Basel) | year= 2018 | volume= 18 | issue= 6 | pages= | pmid=29865289 | doi=10.3390/s18061802 | pmc=6022164 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29865289 }} </ref> or may not<ref name="pmid29632098">{{cite journal| author=Raichle CJ, Eckstein J, Lapaire O, Leonardi L, Brasier N, Vischer AS | display-authors=etal| title=Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement). | journal=Hypertension | year= 2018 | volume= 71 | issue= 6 | pages= 1164-1169 | pmid=29632098 | doi=10.1161/HYPERTENSIONAHA.117.10647 | pmc= | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29632098 }} </ref> be able to determine a subject's blood pressure. | ||
Imaging may also be able to detect: | |||
* Heart rate<ref name="LomalizaPark2019">{{cite journal|last1=Lomaliza|first1=Jean-Pierre|last2=Park|first2=Hanhoon|title=Improved Heart-Rate Measurement from Mobile Face Videos|journal=Electronics|volume=8|issue=6|year=2019|pages=663|issn=2079-9292|doi=10.3390/electronics8060663}}</ref><ref name="pmid29865289">{{cite journal| author=Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR| title=Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate. | journal=Sensors (Basel) | year= 2018 | volume= 18 | issue= 6 | pages= | pmid=29865289 | doi=10.3390/s18061802 | pmc=6022164 | url=https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&tool=sumsearch.org/cite&retmode=ref&cmd=prlinks&id=29865289 }} </ref> | |||
* [[Ethanol intoxication]]<ref name="HermosillaVerdugo2018">{{cite journal|last1=Hermosilla|first1=Gabriel|last2=Verdugo|first2=José Luis|last3=Farias|first3=Gonzalo|last4=Vera|first4=Esteban|last5=Pizarro|first5=Francisco|last6=Machuca|first6=Margarita|title=Face Recognition and Drunk Classification Using Infrared Face Images|journal=Journal of Sensors|volume=2018|year=2018|pages=1–8|issn=1687-725X|doi=10.1155/2018/5813514}}</ref>. | |||
* [[Cushing's syndrome]]<ref name="KosilekFrohner2015">{{cite journal|last1=Kosilek|first1=R P|last2=Frohner|first2=R|last3=Würtz|first3=R P|last4=Berr|first4=C M|last5=Schopohl|first5=J|last6=Reincke|first6=M|last7=Schneider|first7=H J|title=Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives|journal=European Journal of Endocrinology|volume=173|issue=4|year=2015|pages=M39–M44|issn=0804-4643|doi=10.1530/EJE-15-0429}}</ref> | |||
* [[Acromegaly]]<ref name="KosilekFrohner2015">{{cite journal|last1=Kosilek|first1=R P|last2=Frohner|first2=R|last3=Würtz|first3=R P|last4=Berr|first4=C M|last5=Schopohl|first5=J|last6=Reincke|first6=M|last7=Schneider|first7=H J|title=Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives|journal=European Journal of Endocrinology|volume=173|issue=4|year=2015|pages=M39–M44|issn=0804-4643|doi=10.1530/EJE-15-0429}}</ref> | |||
==See also== | ==See also== | ||
Revision as of 03:09, 28 April 2020
In telemetry, Remote sensing technology is defined as the "observation and acquisition of physical data from a distance by viewing and making measurements from a distance or receiving transmitted data from observations made at distant location."[1]
Medical imaging
Transdermal optical imaging, using video from a smartphone camera and using advanced machine learning may[2][3] or may not[4] be able to determine a subject's blood pressure.
Imaging may also be able to detect:
See also
External links
References
- ↑ Anonymous (2025), Remote sensing technology (English). Medical Subject Headings. U.S. National Library of Medicine.
- ↑ Luo H, Yang D, Barszczyk A, Vempala N, Wei J, Wu SJ; et al. (2019). "Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology". Circ Cardiovasc Imaging. 12 (8): e008857. doi:10.1161/CIRCIMAGING.119.008857. PMID 31382766.
- ↑ 3.0 3.1 Gonzalez Viejo C, Fuentes S, Torrico DD, Dunshea FR (2018). "Non-Contact Heart Rate and Blood Pressure Estimations from Video Analysis and Machine Learning Modelling Applied to Food Sensory Responses: A Case Study for Chocolate". Sensors (Basel). 18 (6). doi:10.3390/s18061802. PMC 6022164. PMID 29865289.
- ↑ Raichle CJ, Eckstein J, Lapaire O, Leonardi L, Brasier N, Vischer AS; et al. (2018). "Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement)". Hypertension. 71 (6): 1164–1169. doi:10.1161/HYPERTENSIONAHA.117.10647. PMID 29632098.
- ↑ Lomaliza, Jean-Pierre; Park, Hanhoon (2019). "Improved Heart-Rate Measurement from Mobile Face Videos". Electronics. 8 (6): 663. doi:10.3390/electronics8060663. ISSN 2079-9292.
- ↑ Hermosilla, Gabriel; Verdugo, José Luis; Farias, Gonzalo; Vera, Esteban; Pizarro, Francisco; Machuca, Margarita (2018). "Face Recognition and Drunk Classification Using Infrared Face Images". Journal of Sensors. 2018: 1–8. doi:10.1155/2018/5813514. ISSN 1687-725X.
- ↑ 7.0 7.1 Kosilek, R P; Frohner, R; Würtz, R P; Berr, C M; Schopohl, J; Reincke, M; Schneider, H J (2015). "Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives". European Journal of Endocrinology. 173 (4): M39–M44. doi:10.1530/EJE-15-0429. ISSN 0804-4643.