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Effect of LED Lighting Illuminance and Correlated Color Temperature on Working Memory

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This study was conducted to verify how the illuminance and correlated color temperature of LED lighting affect working memory. For this study, an automatic LED lighting device based on a light sensor was developed and used, and the lighting conditions were treated with a total of six conditions (2 × 3): two illuminance conditions (dim: 400 lx, bright: 1,000 lx) and three correlated color temperature conditions (3,000 K, 5,000 K, and 7,000 K). There were 30 participants in the study, and the average age was 21.6 years (Standard deviation = 1.92). Participants were assigned to all six lighting conditions, and the placement order was randomized. For the measurement of working memory, 3-back task was used and the correct responses for 5 minutes were used as a dependent variable. As a result of repeated measures analysis of variance (ANOVA), both illuminance and correlated color temperature were found to be significant variables affecting working memory, and no interaction effect between illuminance and correlated color temperature was found. As a result of the post hoc verification conducted thereafter, the working memory performance in the bright light condition (1,000 lx) was 48.32 (Standard deviation = 15.63) on average, compared to 44.80 (Standard deviation = 15.29) in the relatively dim condition (400 lx). It was found that the condition of bright light was superior in performing working memory compared to relatively dim condition. The working memory performance in the correlated color temperature condition (5,000 K) was 48.32 (Standard deviation = 16.41) on average and higher than that of other color temperature conditions. As a result, working memory performance was the best in 1,000 lx, 5,000 K condition Mean = 53.43 (Standard deviation = 18.38), and 400 lx, 7,000 K condition Mean = 42.73 (Standard deviation = 17.68) showed the worst performance of working memory.
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Research Article
Effect of LED Lighting Illuminance and Correlated Color
Temperature on Working Memory
Chung Won Lee
1
and Jin Ho Kim
2
1
Dongguk University Gyeongju Campus, Gyeongju, Republic of Korea
2
Kongju National University, Cheonan, Republic of Korea
Correspondence should be addressed to Jin Ho Kim; kjh@kongju.ac.kr
Received 5 July 2020; Revised 28 August 2020; Accepted 25 September 2020; Published 23 October 2020
Academic Editor: Wonho Jhe
Copyright ©2020 Chung Won Lee and Jin Ho Kim. is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
is study was conducted to verify how the illuminance and correlated color temperature of LED lighting affect working memory.
For this study, an automatic LED lighting device based on a light sensor was developed and used, and the lighting conditions were
treated with a total of six conditions (2 ×3): two illuminance conditions (dim: 400 lx, bright: 1,000 lx) and three correlated color
temperature conditions (3,000 K, 5,000 K, and 7,000 K). ere were 30 participants in the study, and the average age was 21.6 years
(Standard deviation 1.92). Participants were assigned to all six lighting conditions, and the placement order was randomized.
For the measurement of working memory, 3-back task was used and the correct responses for 5 minutes were used as a dependent
variable. As a result of repeated measures analysis of variance (ANOVA), both illuminance and correlated color temperature were
found to be significant variables affecting working memory, and no interaction effect between illuminance and correlated color
temperature was found. As a result of the post hoc verification conducted thereafter, the working memory performance in the
bright light condition (1,000 lx) was 48.32 (Standard deviation15.63) on average, compared to 44.80 (Standard
deviation 15.29) in the relatively dim condition (400 lx). It was found that the condition of bright light was superior in
performing working memory compared to relatively dim condition. e working memory performance in the correlated color
temperature condition (5,000 K) was 48.32 (Standard deviation 16.41) on average and higher than that of other color tem-
perature conditions. As a result, working memory performance was the best in 1,000 lx, 5,000 K condition Mean 53.43 (Standard
deviation 18.38), and 400 lx, 7,000 K condition Mean 42.73 (Standard deviation 17.68) showed the worst performance of
working memory.
1. Introduction
Interest in the effects of light on humans has been
accelerated by the advent of LED lighting. e reason is that
LED lighting is easy to operate and can be easily applied to
various scenes, making it suitable for light research.
Moreover, as the well-being culture of modern people has
spread, the desire to actively construct an optimal light
environment, not just for lighting, has promoted light
studies. Various light studies have reported that light affects
human emotions, as well as arousal, sleep, fatigue, cognitive
performance, and memory [1–5]. Particularly, memory is a
very important topic in modern society. e reason is that
memory is closely related to learning represented by
education and dementia, one of the problems of modern
society. Accordingly, many studies have focused on the
relationship between light and memory, focusing on
working memory [1, 3, 5].
e characteristics of light can be largely divided into
studies conducted mainly on the illuminance of light, studies
conducted on the color temperature and color of light, and
studies that are not systematic, but consider the illuminance
and color temperature of light together. Most studies dealing
with intensity of illumination and working memory show
that bright light is more effective in working memory than
relatively dim light, but some studies have not found im-
provement in working memory in bright light or rather
reported that the performance was reduced in bright light
Hindawi
International Journal of Optics
Volume 2020, Article ID 3250364, 7 pages
https://doi.org/10.1155/2020/3250364
[6–8]. For example, Huiberts et al. measured work memory
through numerical width tasks at 200 lx and 1,000 lx illu-
minance in the morning and afternoon hours. As a result, in
the easy task measured in the afternoon, bright light con-
dition induces improvement of working memory. However,
in the difficult task experiment conducted in the afternoon,
contrary research results showed that bright light rather
degrades performance [3]. In addition, a study by Smolders
and de Kort, which studied the effects of light illuminance
(200 lx vs. 1,000 lx) and fatigue on work memory showed
that the correct response of the task was effective at relatively
dim 200 lx [6].
On the other hand, studies dealing with the color
temperature of light and working memory show more mixed
results. Kenz verified the effect of the color temperature of
lighting on free recall and problem solving. As a result, free
recall was the best in the 3,000 K condition and relatively low
and no significant difference was observed in the 4,000 K
condition and the 5,500 K condition. And the problem-
solving ability was also the best in the color temperature
condition of 3,000 K and performed well in the order of
4,000 K condition and 5,500 K condition. ey reported that
the color temperature condition of 3,000 K had a superior
performance difference than the other conditions [9]. In
addition, Zhu et al. measured working memory under the
conditions of 3,000 K and 6,500 K and found that the color
temperature condition of 3,000 K performed better than the
6,500 K illumination [10]. However, in the study of the effect
of color temperature on cognitive performance in the office
environment conducted by Knez and Enmarker, no sig-
nificant difference was found between 3,000 K (reddish) and
4,000 K (bluish) conditions [11]. Other studies have reported
that color temperature conditions above 6,000 K are ex-
cellent for attention and working memory. Hawes et al.
reported that performance of work memory such as symbol
identification and color recognition task was the best at high
color temperature conditions of 6,029 K [12]. And the study
of Chellappa et al. showed that cognitive performance was
the best under 6,500 K conditions [2].
As such, studies between color temperature and working
memory to date report much more contradictory results
than studies between illuminance and working memory. It
can be presumed that the cause is due to the variety of
experimental environments including lighting and working
memory tasks. is is considered to be a phenomenon that
occurs because studies between color temperature and
working memory are still insufficient. In fact, there were
many contradicting research results in the early days of the
study of illuminance as well, but as more studies were
conducted, it was proved in the direction that working
memory was excellent in bright light conditions.
Although a minority, some studies have taken into ac-
count both illuminance and color temperature to explore the
optimal lighting environment for working memory. Zhu
et al. focused on two illuminance conditions (200 lx vs. 1,200
lx) and two color temperature conditions (3000 K vs.
6,500 K) to see how illuminance conditions affect working
memory in the morning and during the daytime and verified
it. As a result, it was found that the response of the cognitive
performance task was the slowest at 200 lx and 6,500 K, and
the accuracy of the cognitive performance task was higher in
the bright lighting condition than in the dim lighting
condition [10]. In addition, Ru et al. also evaluated cognitive
performance under two illuminance conditions (100 lx vs.
1,000 lx) and two color temperature conditions (3,000 K vs.
6,500 K). In this study, the reaction velocity of the cognitive
performance task was found to be excellent in the relatively
bright condition of 1,000 lx. However, no statistical dif-
ference was found between the color temperature conditions
[13].
However, most of the previous studies have been con-
ducted in an excessively extreme lighting environment, and
some studies have been designed for binominal experiments
such as bright and dim light, cool color temperature, and
warm color temperature. Binomial experimental design can
verify the effect of light on memory, but there is a limitation
that the binomial experimental design cannot be explained
with respect to detailed aspects. In addition, many studies
have verified the effectiveness of working memory through
independent conditions of illuminance or color tempera-
ture. Although some studies have considered the illumi-
nance and color temperature together, the number is very
small, and the results are very mixed. Since both the light
illuminance and the color temperature are variables that can
affect memory, it is possible to search for an optimal lighting
environment for working memory only when the illumi-
nance and color temperature are considered together.
erefore, this study was conducted to systematically verify
the effect of light on working memory through a more
continuous experimental design while considering the il-
luminance and color temperature together with the focus on
LED lighting. Besides, this study seeks to explore and
propose the optimal lighting environment for working
memory applicable to real life.
2. Materials and Methods
2.1. Participants. ere were 30 experiment participants, 19
men and 11 women, and the average age was 21.6 years
(SD 1.92). Prior to the experiment, the orientation and task
implementation method throughout the experiment were
explained, and the tasks to be performed in order to improve
the adaptability of the experiment were preliminarily con-
ducted. rough this, the participants’ cognitive impairment
and the suitability of the participation were verified.
In addition, the following measures were taken to
control other variables that may affect the experiment
participants and to ensure consistent condition of the
participants. Priorly, before the participants participated in
the experiment, we received a pledge from the participants.
e contents of the pledge included an explanation of the
cost of participating in the experiment, as well as the content
of getting enough sleep and refraining from drinking alcohol
or caffeine if possible. e cost of participating in the ex-
periment was quite incentive from the participant’s point of
view, and it was said that if the participant violated the
request or did not actively perform the experiment, the
experimental participation cost was not paid. rough this,
2International Journal of Optics
efforts were made to strictly control the participation of
experimental participants.
And we asked about the condition of the participants
verbally before the experiment. It was checked whether there
was any situation in violation of the request, including sleep,
and whether the current participant’s subjective condition
was in a state sufficient for the experiment.
2.2. Experiment Environment. For this experiment, an au-
tomatic LED lighting device based on a light sensor and a
lighting control system developed to construct a systematic
lighting environment were developed and used. e
structure of the automatic lighting device consists of a power
switch, a power connection jack, a sensor part, an inner heat
sink, a light diffuser plate, and an attached briquette as
shown in Figure 1. And a color temperature sensor and an
illuminance sensor were attached to the sensor portion as
shown in the figure at the corner of the lighting. e input
power of this device is 24VDC, and the high-power LED was
used as the light source. e light power is approximately
10–20 W, and the LED Mounting Type is equipped with a
separate steel bracket. e case material was made of alu-
minum, and the operating temperature was designed to be
operated at 10°C70°C. e automatic lighting device is
equipped with 2.4 GHz Wireless, so it can be controlled in
both PC and mobile environments. In addition, the standard
of the automatic lighting device is 700 ×60 ×45 (mm). e
lighting was in the form of a stand and was used in the form
of local lighting to illuminate the surface of the desk. e
illumination conditions were treated with two illuminance
conditions (400 lx dim light vs. 1,000 lx bright light) and
three correlated color temperature conditions (3,000 K,
5,000 K, and 7,000 K). e spectral power distribution
according to the correlated color temperature is shown in
Figure 2. An experiment environment such as Figure 3 was
formed for this study. In the lab, light from other sources was
blocked using a light-blocking curtain. e experimental
laboratory temperature was maintained at 24°C±4°C and
50% ±10% humidity to meet PMV conditions of ASHRAE
standards. In addition, lighting conditions were treated
based on the identification mark (+) marked in the middle of
the desk as shown in Figure 3.
2.3. Work Memory Measurement Tool. Working memory
was measured through the n-back test, one of the number-
wide tasks. e n-back task is often used in psychology and
cognitive neuroscience as a task to measure work memory or
a part of work memory, first introduced by Kirchner [14].
e n-back task is a task for determining whether a con-
tinuous stimulus is given and the current stimulus matches
the previous n-th order stimulus. As nof the n-back task
increases, the task difficulty increases, and the difficulty of
the task is adjusted through n. In this experiment, working
memory was evaluated through 3-back task.
2.4. Experiment Procedure. In this study, through the re-
peated measure experiment design, all participants partic-
ipated in all six experimental conditions (A condition: 400
lx, 3,000 K, B condition: 400 lx, 5,000 K, C condition: 400 lx,
7,000 K, D condition: 1,000 lx, 3,000 K, E condition: 1,000 lx,
5,000 K, F condition: 1,000 lx, 7,000 K) allocated randomly.
First, the participants adapted to the lighting environment
through 2 minutes of dark adaptation and 2 minutes of light
adaptation. Subsequently, 3-back task was performed for 5
minutes, and the number of correct answer responses among
the 3-back tasks was used as a dependent variable.
is experiment was conducted only for 4 hours from 2
pm to 6 pm in consideration of postprandial drowsiness, etc.
In order to minimize the interference of the previous ex-
periment, the test participants were assigned to only one
experimental condition per day.
2.5. Statistical Analysis Method. Descriptive statistics were
calculated for all the variables. en, to analyze the effect of
illuminance and color temperature on working memory,
repeated measures analysis of variance(ANOVA) was per-
formed on two illuminance conditions and three color
temperatures. Also, the difference in working memory
according to the six lighting conditions was performed
through a single level of repeated measures analysis of
variance (ANOVA). Post hoc analysis was performed using
the least significant difference (LSD) method. Significance
was defined as p<0.05.
3. Result and Discussion
As a result of descriptive statistical analysis of working
memory according to illuminance and correlated color
temperature, as shown in Table 1, the average number of
correct responses was 53.4 (Standard deviation 18.38)
under 1,000 lx and 5,000 K lighting conditions, indicating
that the performance of working memory was the best. On
the other hand, at 400 lx and 7,000 K, the average perfor-
mance was 42.73 (Standard deviation 17.68), indicating
that the performance of working memory was the lowest.
Repeated measures analysis of variance (ANOVA) was
performed to analyze the effect of the illuminance and
correlated color temperature of LED lighting on working
memory. As a result of variance analysis, both the illumi-
nance (F5.36, p<0.03) and the correlated color temper-
ature (F7.34, p<0.00), as shown in Table 2, were found to
be significant variables affecting working memory at 95%
confidence level. However, there was no interaction effect
between illuminance and color temperature.
Subsequently, difference verification was performed
through the least significant difference (LSD) method to
analyze the difference between each condition of illumi-
nance and correlated color temperature on working
memory. As a result of analysis, the illuminance of working
memory was found to be significantly better than 1,000 lx
International Journal of Optics 3
Power connection jack
Attached briquette
Inner heat sink
Light diuser
Sensor part
Power switch
Figure 1: Automatic LED lighting device based on light sensors.
400
0
10000
20000
30000
40000
500 600 700
Wavelength (nm)
Intensity (a.u.)
3000K
5000K
7000K
Figure 2: Spectral power distribution.
Experimental room
Blackout curtains
LED light
Participant Desk
Reference point
Waiting
room
Experimental space
360
200 420
Figure 3: Experimental laboratory.
4International Journal of Optics
condition (M48.32, SD 2.85) than 400 lx condition
(M44.80, SD 2.80) as shown in Table 3. ese results can
be said to support previous studies of excellent working
memory in relatively bright light.
And, after the correlated color temperature difference
verification, it was found that there is a statistically signif-
icant difference between 3,000 K condition (Mean 44.72,
Standard deviation 2.97) and 5,000 K condition
Table 1: Descriptive statistics of working memory according to light illuminance and correlated color temperature (CCT).
Illuminance CCT 400 lx 1,000 lx Total N
Mean Standard deviation Mean Standard deviation Mean Standard deviation
3,000 K 43.70 17.00 45.73 18.77 44.72 16.24 30
5,000 K 48.97 18.51 53.43 18.38 50.70 16.41 30
7,000 K 42.73 17.68 45.80 17.73 44.27 15.40 30
Total 44.80 15.29 48.32 15.63
Table 2: Analysis of the effect of illuminance and correlated color temperature (CCT) on working memory.
Sum of square Degree of freedom Mean square F p
Illuminance 558.27 1 558.27 5.36 0.03
CCT 1547.81 2 773.91 7.34 0.00∗∗
Illuminancecolor temperature 93.08 2 45.54 0.31 0.74
p<0.05, ∗∗p<0.01.
Table 3: Difference verification of illuminance on working memory.
Mean difference (I-J) Standard error p
1,000 lx (I) vs. 400 lx (J) 3.52 1.52 0.03
p<0.05, ∗∗p<0.01.
Table 4: Difference verification of correlated color temperature on working memory.
Mean difference (I-J) Standard error p
5,000 K(I) vs. 3,000 K(J) 5.98 1.74 0.00∗∗
5,000 K(I) vs. 7,000 K(J) 6.43 1.76 0.00∗∗
3,000 K(I) vs. 7,000 K(J) 0.45 2.10 0.83
p<0.05, ∗∗p<0.01.
Light condition
4001x, 3,000K
4001x, 5,000K
4001x, 7,000K
1,0001x, 3,000K
1,0001x, 5,000K
1,0001x, 7,000K
35
40
45
50
55
60
Working memory (A number of times)
Mean SD
Figure 4: Difference in working memory according to light illuminance and color temperature condition.
International Journal of Optics 5
(Mean 50.7, Standard deviation 3.0), 5,000 K condition
and 7,000 K condition (Mean 44.27, Standard
deviation 2.81), and the 5,000 K condition had the most
positive effect on working memory performance as shown in
Table 4.
In summary, 1,000 lx (5,000 K) condition was the best for
performing working memory according to the illuminance
and the correlated color temperature as shown in Figure 4.
In the order of 400 lx (5,000 K) condition, 1,000 lx (3,000 K),
and 1,000 lx (7,000 K) conditions, it was found to be an
excellent lighting environment for working memory. On the
other hand, the condition of 400 lx (7,000 K) was the worst
lighting environment for working memory.
rough this study, it was found that the working
memory is the best in the condition of relatively bright 1,000
lx in illuminance and the best in the 5,000 K condition in
color temperature. ese results support the previous studies
that working memory is excellent in bright light. However,
in terms of color temperature, it can be said that the result is
inconsistent with the previous study that showed that
working memory was excellent at about 3,000 K or 4,000 K,
or above 6,000 K. In addition, in this study, working memory
was found to be the worst under a color temperature of
7,000 K. is seems to need to be repeatedly verified through
more systematic research.
And through this experiment, the best LED lighting
condition for working memory was found to be 1,000 lx
(5,000 K).
4. Conclusion
is study was conducted to verify the effect of illuminance
and correlated color temperature on working memory and
to explore the optimal lighting environment that can activate
working memory. As a result of the study, both the illu-
minance and the correlated color temperature were sig-
nificant variables affecting working memory, and among the
lighting environments used in the experiment, a lighting
condition of 1,000 lx (5,000 K) was found to be the best
lighting for working memory. e more specific results are
as follows.
First, through this study, it was verified that the per-
formance of working memory is excellent in a relatively light
condition. ese results can be said to support previous
studies of excellent working memory at high illuminance.
Second, in this study, the 5,000 K condition was found to
have a positive effect on working memory in correlated color
temperature.
Finally, through this study, the 1,000 lx (5,000 K) lighting
environment was found to be a better lighting condition for
working memory than other conditions. Of course, the
lighting conditions used in this study still have limitations to
represent all lighting. However, this study is significant in
that it explores the working memory performance by
considering the illuminance and color temperature together
and suggests a standard for a lighting environment that can
be maximized in the working memory.
Despite these findings, this study still has a limitation in
not considering various variables that can affect working
memory due to experimental conditions. First, this exper-
iment did not consider temporal variables like morning and
afternoon. As shown in the study of Huiberts et al. [3], there
is a possibility that the performance of tasks in the morning
and afternoon affects working memory. However, in this
study, the experiment was conducted only in the afternoon
hours from 2 o’clock to 6 o’clock. It is necessary to clarify
how illuminance and difference in color temperature affect
working memory in the morning and afternoon through
future research. Second, the participants of this experiment
were mainly college students in their early twenties.
Memories are clearly different depending on the age group,
such as young people and the elderly, and there is a good
possibility that the effects of illumination and color tem-
perature are also different. In future studies, it is judged that
there is a need to organize a more systematic experiment for
experiment participants of a wider range of ages. Finally, in
future studies, it is considered that there is a need to further
strengthen the control over the experiment in the laboratory
environment and experiment participants. Although not
considered in this study, if the amount of Co
2
in the lab-
oratory and the stress measurement of the experimental
participant are included, it is considered that a more sys-
tematic study could be achieved.
Data Availability
e data used to support the findings of this study are in-
cluded within the supplementary information file.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
Acknowledgments
is Research was supported by the National Research
Foundation of Korea (no. 2018R1D1A1B07050211).
Supplementary Materials
is data are the result of working memory measured
under each lighting condition through experiments. e
working memory was measured through 3-back task, and it
is the number that responded correctly for 5 minutes.
(Supplementary Materials)
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International Journal of Optics 7
... Conversely, other researchers have even found that a low CCT level (3000 K) might improve short-term memory [98]. In any case, the extensive variety of lighting environments and of working memory tasks makes further research into the subject necessary [99]. ...
... First, the psychological metrics showed significant differences in memory task performance, which suggests that ILL and CCT should not be examined in isolation, as they produce a combined effect. Some authors, such as C. W. Lee et Kim [99], have also indicated that, in the search for an optimal lighting environment for working memory, ILL and CCT must be analysed in combination, given that both lighting variables can affect memory. ...
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In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mED I), melanopic daylight (D65) efficacy ratio (mDER), and four other parameters. Due to the importance of mED I and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions(SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mED I calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model reached ±3.3% after matrix transformation and illuminance processing combined with the successful mDER calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter mED I using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research.
... For the calculation of the RGB channel signals it is therefore necessary to know or to During the spectral measurement of the RGB sensor, one can determine the spec- . 235 tral sensitivity for the red color channel, for example, according to Equation 16. ...
Preprint
Full-text available
In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER) and 4 other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model has reached ± 3.3% after an appropriate matrixing process and proper illumination characterization combined with the successful mDER calculation model. This result opens the potential of low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other researches.
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In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, which were first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mED I), melanopic daylight (D65) efficacy ratio (mDER), and four other parameters. Due to the importance of mED I and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions(SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mED I calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model reached ±3.3% after matrix transformation and illuminance processing combined with the successful mDER calculation model. This result opens the potential for low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate for the non-visual effective parameter mED I using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other research.
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