Circadian metric – Computation of circadian stimulus using illuminance, correlated colour temperature and colour rendering index

Truong, William; Zandi, Babak; Trinh, Quang Vinh; Khanh, Tran Quoc

doi:10.1016/j.buildenv.2020.107146

Cited by 23 publications

(16 citation statements)

References 34 publications

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“…Similar results were obtained with the intrasubject dataset, indicating the validity of the proposed pupil modelling concept. Specifically, the first input variant could make a simplified application possible 78 since only the CIExy-2° chromaticity points and the luminance of a stimulus is necessary for determining the base function's model parameters and reconstructing the temporal pupil light response.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based pupil model predicts time and spectral dependent light responses

Zandi

Khanh

2021

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Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil’s time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 $$\pm$$ ± 1 K, 4983 $$\pm$$ ± 3 K, 10,138 $$\pm$$ ± 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m2. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based pupil model predicts time and spectral dependent light responses

Zandi

Khanh

2021

Sci Rep

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“…The CCTs between 2700 and 7000 K are of particular interest, as most studies on indoor lighting have selected chromaticity coordinates in this range [94,95,97]. Therefore, we used 561 chromaticity coordinates along the Planckian locus between 2700 and 7443 K ±Duv 0 to 0.048 as optimisation targets, reused from a recent publication by Truong et al [80] (Figure 2a). Truong et al provides the chromaticity targets in the CCT range of our interest with a sufficiently high span above and below the Planckian locus.…”

Section: The Melanopic Limits Of Metamer Spectramentioning

confidence: 99%

“…We report the degree of freedom in varying the melanopic radiance at steady chromaticity coordinates and luminance. For this purpose, we have calculated over 1.2 million using 561 chromaticity coordinates as optimisation targets [80], which were located along the Planckian locus (2700 K to 7443 K) for ±Duv 0 to 0.048. We used 6-channel, 8-channel, and 11-channel LED combination at the luminance levels 140 cd/m 2 , 180 cd/m 2 and 220 cd/m 2 for optimisation.…”

Section: Introductionmentioning

confidence: 99%

Melanopic Limits of Metamer Spectral Optimisation in Multi-Channel Smart Lighting Systems

et al. 2021

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Modern indoor lighting faces the challenge of finding an appropriate balance between energy consumption, legal requirements, visual performance, and the circadian effectiveness of a spectrum. Multi-channel LED luminaires have the option of keeping image-forming metrics steady while varying the melanopic radiance through metamer spectra for non-visual purposes. Here, we propose the theoretical concept of an automated smart lighting system that is designed to satisfy the user’s visual preference through neural networks while triggering the non-visual pathway via metamers. To quantify the melanopic limits of metamers at a steady chromaticity point, we have used 561 chromaticity coordinates along the Planckian locus (2700 K to 7443 K, ±Duv 0 to 0.048) as optimisation targets and generated the spectra by using a 6-channel, 8-channel, and 11-channel LED combination at three different luminance levels. We have found that in a best-case scenario, the melanopic radiance can be varied up to 65% while keeping the chromaticity coordinates constant (Δu′v′≤7.05×10−5) by using metamer spectra. The highest melanopic metamer contrast can be reached near the Planckian locus between 3292 and 4717 K within a Duv range of −0.009 to 0.006. Additionally, we publish over 1.2 million optimised spectra generated by multichannel LED luminaires as an open-source dataset along with this work.

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“…This subset of ganglion cells are part of the non-image-forming mechanism of the eye because of their projection to regions of the suprachiasmatic nucleus (SCN) and olivary pretectal nucleus (OPN) (Ruby et al, 2002;Berson, 2003;Hattar et al, 2003;Do et al, 2009;Ecker et al, 2010;Allen et al, 2019;Do, 2019). As a result, the ipRGCs can modulate the circadian rhythm (Freedman, 1999;Brainard et al, 2001;Thapan et al, 2001;Rea and Figueiro, 2018;Truong et al, 2020) and pupil light response (Lucas et al, 2001(Lucas et al, , 2020Gamlin et al, 2007;Young and Kimura, 2008;Barrionuevo et al, 2018;Murray et al, 2018) via a processing path that works independently of the classical image-forming pathway (Hattar et al, 2006;Güler et al, 2008;Schmidt et al, 2014;Spitschan, 2019a). Recent studies showed that the pupil light response cannot be described by the V(λ) weighted luminance alone, making a revision of classical pupil models necessary (Zandi et al, 2018(Zandi et al, , 2020Spitschan, 2019b;Zele et al, 2019).…”

Section: The Rising Popularity Of Pupil Light Response Researchmentioning

confidence: 99%

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

et al. 2021

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The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

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Circadian metric – Computation of circadian stimulus using illuminance, correlated colour temperature and colour rendering index

Cited by 23 publications

References 34 publications

Deep learning-based pupil model predicts time and spectral dependent light responses

Deep learning-based pupil model predicts time and spectral dependent light responses

Melanopic Limits of Metamer Spectral Optimisation in Multi-Channel Smart Lighting Systems

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

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