Detection of ultraviolet B radiation with internal smartphone sensors

Turner, Joanna; Parisi, Alfio V.; Igoe, Damien P.; Amar, Abdurazaq

doi:10.1080/10739149.2017.1298042

Cited by 12 publications

(20 citation statements)

References 34 publications

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“…However, color measurements still depend on the incident light spectrum [25]; hyperspectral measurements, for example with iSPEX [10], and characterization of common light sources [1,78] may provide valuable additional information. Finally, while no significant response was found at wavelengths <390 or >700 nm on our test cameras, it may be worthwhile in the future and the SPECTACLE database to use a spectral range of 380-780 nm to follow colorimetric standards [25,52,56].…”

Section: Discussionmentioning

confidence: 79%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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Section: Discussionmentioning

confidence: 79%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

View full text Add to dashboard Cite

show abstract

“…Smartphones have additionally been applied quantitatively in this context, for example in determining ‘leaf area index’, which is a measure of foliage cover [ 18 ], and these units could be powerful tools in tracking longer term trends in sky [ 19 ], land cover and vegetation conditions. Ultraviolet characterisation with these devices has been performed too, with a view to potential future use in determining personal UV exposure levels [ 20 , 21 , 22 , 23 ]. Finally, there has also been considerable use of array sensors, developed for the smartphone market, but instead housed within camera modules, which are interfaced with low cost computer, e.g., Raspberry Pi, boards, for application in a number of scientific domains, e.g., volcanology [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Smartphone Spectrometers

McGonigle

Wilkes

Pering

et al. 2018

Sensors

127

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Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a ‘lab in a phone’ capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades.

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“…This has been first validated by means of laboratory tests using an irradiation monochromator [60], then with inthe-field solar radiation [61] and an app was also designed to measure UVA aerosol optical depth and direct solar irradiance at the wavelengths 340 nm and 380 nm [62]. The same research group has also shown the feasibility of UVB wavelengths measurement using smartphone [63]. The phone camera was equipped with a 305 nm bandpass filter and the red component was found the parameter providing the highest signal-to-noise ratio.…”

Section: High-resolution Cameramentioning

confidence: 99%

“…Despite the increasing interest in smartphone based sensing systems, there are also some gaps that from [190]. colorimetric alcohol concentration in saliva 0 -0.3% [38] colorimetric pH, protein and glucose in urine 5 -9, 0 -100 mg/dL, 0 -300 mg/dL [39] colorimetric blood hematocrit level 10 -65% [41] colorimetric streptomycin concentration in food 50 -267 nM [42] colorimetric BSA, catalase enzyme and carbohydrate 0 -1 mg/mL, 0 -1 mg/mL, 0 -140 µg/mL [43] colorimetric cloud coverage 4 -98% [48] colorimetric surface corrosion of iron N/A [50] irradiance measurement UVA solar irradiance 0 -10 mW/m 2 [60] irradiance measurement UVA aerosol optical depth 0.05 -0.20 [61] irradiance measurement UVB solar irradiance 1 -9 mW/m 2 [63] irradiance measurement atmospheric total ozone column 260 -320 DU [65] irradiance measurement SO 2 volcanic emission 0 -3. computer vision bacterial colony counter N/A [80] computer vision bacterial colony counter 0 -250 CFU [81] computer vision bacterial colony counter N/A [82] computer vision bacterial colony counter 0 -500 CFU [83] computer vision surveillance of fruit flies N/A [84] computer vision food recognition tool N/A [85] computer vision food recognition and nutritional value N/A [86] computer vision heart rate measurement N/A [87] mobile microscopy cell imaging (brightfield and fluorescent) N/A [88] mobile microscopy image analysis of green algae in freshwater N/A [89] sound recording and analysis chronic lung diseases average error 5.1%, detection rate 80 -90% [90] sound recording and analysis chronic lung diseases average error 8.01% [91] sound recording and analysis number of coughs detection rate 92% [92] sound recording and analysis respiratory rate estimation error < 1% [93] sound recording and analysis nasal symptoms N/A [94] sound recording and analysis snoring quantification correlation > 0.9 [95] sound recording and analysis hearing threshold in noisy environment N/A …”

Section: Prospects and Challengesmentioning

confidence: 99%

A sensor-centric survey on the development of smartphone measurement and sensing systems

Grossi

2019

Measurement

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Modern mobile phones, featuring high performance microprocessors, rich set of sensors and internet connectivity are largely diffused all over the world and are ideal devices for the development of low-cost sensing systems, in particular for low-income developing countries and rural areas that lack the access to diagnostic laboratories and expensive instrumentation. In the design of a smartphone based sensing system different elements must be taken in consideration such as sensors performance, acquisition rate and privacy preserving strategies when personal data must be shared in the cloud. In this paper a sensor-centric survey on smartphone based sensing systems is presented, covering different fields of application. Two different development approaches will be discussed: 1) the exploitation of the large number of sensors embedded in modern smartphones (high-resolution camera, microphone, accelerometer, gyroscope, magnetometer, GPS); 2) the interfacing with external sensors that communicate with the smartphone by the embedded wireless or wired communication technology.

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Detection of ultraviolet B radiation with internal smartphone sensors

Cited by 12 publications

References 34 publications

Standardized spectral and radiometric calibration of consumer cameras

Standardized spectral and radiometric calibration of consumer cameras

Smartphone Spectrometers

A sensor-centric survey on the development of smartphone measurement and sensing systems

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