Light is necessary for life, and artificial light improves visual performance and safety, but there is an increasing concern of the potential health and environmental impacts of light. Findings from a number of studies suggest that mistimed light exposure disrupts the circadian rhythm in humans, potentially causing further health impacts. However, a variety of methods has been applied in individual experimental studies of light-induced circadian impacts, including definition of light exposure and outcomes. Thus, a systematic review is needed to synthesize the results. In addition, a review of the scientific evidence on the impacts of light on circadian rhythm is needed for developing an evaluation method of light pollution, i.e., the negative impacts of artificial light, in life cycle assessment (LCA). The current LCA practice does not have a method to evaluate the light pollution, neither in terms of human health nor the ecological impacts. The systematic literature survey was conducted by searching for two concepts: light and circadian rhythm. The circadian rhythm was searched with additional terms of melatonin and rapid-eye-movement (REM) sleep. The literature search resulted to 128 articles which were subjected to a data collection and analysis. Melatonin secretion was studied in 122 articles and REM sleep in 13 articles. The reports on melatonin secretion were divided into studies with specific light exposure (101 reports), usually in a controlled laboratory environment, and studies of prevailing light conditions typical at home or work environments (21 studies). Studies were generally conducted on adults in their twenties or thirties, but only very few studies experimented on children and elderly adults. Surprisingly many studies were conducted with a small sample size: 39 out of 128 studies were conducted with 10 or less subjects. The quality criteria of studies for more profound synthesis were a minimum sample size of 20 subjects and providing details of the light exposure (spectrum or wavelength; illuminance, irradiance or photon density). This resulted to 13 qualified studies on melatonin and 2 studies on REM sleep. Further analysis of these 15 reports indicated that a two-hour exposure to blue light (460 nm) in the evening suppresses melatonin, the maximum melatonin-suppressing effect being achieved at the shortest wavelengths (424 nm, violet). The melatonin concentration recovered rather rapidly, within 15 min from cessation of the exposure, suggesting a short-term or simultaneous impact of light exposure on the melatonin secretion. Melatonin secretion and suppression were reduced with age, but the light-induced circadian phase advance was not impaired with age. Light exposure in the evening, at night and in the morning affected the circadian phase of melatonin levels. In addition, even the longest wavelengths (631 nm, red) and intermittent light exposures induced circadian resetting responses, and exposure to low light levels (5-10 lux) at night when sleeping with eyes closed induced a circadian response. The review enables further development of an evaluation method of light pollution in LCA regarding the light-induced impacts on human circadian system.

Systematic review of light exposure impact on human circadian rhythm

Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. The technology is driven by six main factors: light-emitting diodes (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird’s eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof ’s comfort curve, correlated color temperature, and relative mean square error. Finally, an in-depth discussion of open issues and future challenges in increasing user comfort in smart lighting using activity recognition is also provided.

Machine Learning Methods in Smart Lighting Towards Achieving User Comfort: A Survey

In this study, two experiments were conducted to investigate the effects of the color rendering index (CRI) and correlated color temperature (CCT) of light-emitting diode (LED) lighting on office user acceptance and to explore the proper color attributes for human-centric office lighting. Experiment 1 had four LED lights, with two levels for the CRI (CRI < 80: 79, 76; or CRI ≥ 80: 83, 84) and CCT (3000 K or 6500 K) at 300 lux. In experiment 2, there were four LED lights, with several levels for the CRI (CRI < 80: 78; or CRI ≥ 80: 87, 83) and CCT (3000 K or 6500 K) at 500 lux. Ninety-six participants in experiment 1 and ninety-four participants in experiment 2 performed a reading task. The results in experiment 1 and experiment 2 showed that LEDs with lower CRI values at warm color temperatures were rated as more acceptable than LEDs with higher CRI values at warm color temperatures. However, the positive effect extended to LEDs with higher CRI values at cool temperatures but not to LEDs with lower CRI values at cool temperatures. Therefore, the findings are that LEDs with lower CRI values at warm color temperatures and LEDs with higher CRI values at cool temperatures provide the right level of color attributes for office lighting.

A Randomized Controlled Trail for Comparing LED Color Temperature and Color Rendering Attributes in Different Illuminance Environments for Human-Centric Office Lighting

The unique radiative, photometric and colorimetric characteristic of a light-emitting diode is derived from its spectral power distribution. Modeling such characteristics with respect to the forward current, temperature or operating time has been subject of various studies. Deriving a simple analytical model, however, is not trivial due to the unique emission pattern varying with different types and technologies of light emitting diodes. For this purpose, curve fitting multiple superimposed Gaussian probability density functions to the spectral power distribution is a common approach. Despite excellent $R^{2}$ goodness of fit results, significant deviations within the photometric and colorimetric parameters, such as luminous flux or chromaticity coordinates, are observed. In addition, most studies were conducted on a small sample set of very few different spectral power distributions. This work provides a comprehensive comparison and evaluation of 19 different (superimposed) probability density function based models provided by the literature tested on a total of 15 different spectral power distributions of monochromatic blue, green and red light-emitting diode as well as phosphor-converted spectra of lime, purple and white samples with different correlated color temperatures. All models were evaluated by means of their coefficient of determination, radiant flux, chromaticity coordinate deviation and Bayesian Information Criterion. This study shows that a superimposed (split) Pearson VII model is able to outperform the commonly used Gaussian model approach by far. In addition, an application example in regard of forward current dependence is given to prove the proposed approach.

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Advancements in Spectral Power Distribution Modeling of Light-Emitting Diodes

Influence of location, season and time of day on the spectral composition of ambient light: Investigation for application in myopia

As one factor among others, circadian effectiveness depends on the spatial light distribution of the prevalent lighting conditions. In a typical office context focusing on computer work, the light that is experienced by the office workers is usually composed of a direct component emitted by the room luminaires and the computer monitors as well as by an indirect component reflected from the walls, surfaces, and ceiling. Due to this multi-directional light pattern, spatially resolved light measurements are required for an adequate prediction of non-visual light-induced effects. In this work, we therefore propose a novel methodological framework for spatially resolved light measurements that allows for an estimate of the circadian effectiveness of a lighting situation for variable field of view (FOV) definitions. Results of exemplary in-field office light measurements are reported and compared to those obtained from standard spectral radiometry to validate the accuracy of the proposed approach. The corresponding relative error is found to be of the order of 3–6%, which denotes an acceptable range for most practical applications. In addition, the impact of different FOVs as well as non-zero measurement angles will be investigated.

Measurement of Circadian Effectiveness in Lighting for Office Applications

Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications.

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Light Emitting Diodes are an integral part of modern illumination systems. While their long-term stability in terms of lumen maintenance used to be in the focus leading to drastically increased nominal lifetimes over the past decade, the color shift or respectively chromaticity shift is lately attracting more and more attention especially in lighting applications that demand for a high chromatic stability. Common ways of displaying and reporting color shifts in LEDs and LED products provide only limited possibilities to accurately classify changes in color according to the current understanding of the underlying degradation processes. This paper therefore presents a methodology to overcome the observed discrepancies in analyzing time-dependent chromaticity data in terms of their shift direction and distance within the CIE 1976 ( $u',v'$ ) chromaticity diagram. The use of an extrapolation function extending the shift towards the spectral locus enables the determination of the corresponding nearest spectral locus intersection wavelength. Thereby, it is possible to classify changes in color according to different shift regions, which basically allows for performing an automated comprehensive color shift mode analysis. As part of this paper five different algorithmic approaches for determining the intersection wavelength and shift region are discussed and compared in terms of their accuracy and ease in implementation.

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Combined Methodology for Accurate Evaluation of Distance and Direction of Chromaticity Shifts in LED Reliability Tests

We report on the degradation mechanisms and dynamics of silicone encapsulated ultraviolet A (UV-A) high-power light-emitting diodes (LEDs), with a peak wavelength of <inline-formula> <tex-math notation="LaTeX">${\mathrm {365~ \text {n} \text {m} }}$ </tex-math></inline-formula>. The stress tests were carried out for a period of 8665 hours with forward currents between <inline-formula> <tex-math notation="LaTeX">${\mathrm {350~ \text {m} \text {A} }}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathrm {700~ \text {m} \text {A} }}$ </tex-math></inline-formula> and junction temperatures up to 132°C. Depending on stress condition, a significant decrease in optical power could be observed, being accelerated with higher operating conditions. Devices stressed at a case temperature of 55 °C indicate a decrease in radiant flux between 10- <inline-formula> <tex-math notation="LaTeX">${\mathrm {40~\%}}$ </tex-math></inline-formula> varying with measurement current, whereas samples stressed at higher case temperatures exhibit crack formation in the silicone encapsulant accompanied by electromigration shorting the active region. The analyzed current and temperature dependency of the degradation mechanisms allows to propose a degradation model to determine the device lifetime at different operating parameters. Additional stress test data collected at different aging conditions is used to validate the model’s lifetime predictions.

Simon Benkner

Papers

Measurement of Circadian Effectiveness in Lighting for Office Applications

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Combined Methodology for Accurate Evaluation of Distance and Direction of Chromaticity Shifts in LED Reliability Tests

Advancements in Spectral Power Distribution Modeling of Light-Emitting Diodes

Lifetime Prediction of Current-and Temperature-Induced Degradation in Silicone-Encapsulated 365 nm High-Power Light-Emitting Diodes