TiL Report: Human Centric Lighting – Quo Vadis by DI Johannes Weninger TiL Report: Human Centric Lighting – Quo Vadis by DI Johannes Weninger
Lighting technology goes through a time of groundbreaking changes these days. The extensive application of the compact digital light source LED facilitates the generation... TiL Report: Human Centric Lighting – Quo Vadis by DI Johannes Weninger

Lighting technology goes through a time of groundbreaking changes these days. The extensive application of the compact digital light source LED facilitates the generation of dynamic lighting scenarios with varying intensities, spectra and luminous intensity distributions and the implementation of new lighting designs for personal visual, biological and emotional needs. Integrating sensor-technologies and implementing complex lighting control strategies will further support the transformation of current lighting systems into adaptive lighting solutions that instantaneously react to environmental alterations and individual desires. To start a sustainable transformation process, the lighting industry has to seize the emerging opportunities, connect to information and communication technologies more closely and rethink future lighting concepts. There is a lot at stake, especially for the European lighting industry! In the following the authors present their positions based on own long-standing experiences in light impact research and lighting design.

Introduction
The term human-centric lighting (HCL) was implemented in 2013 as part of a market study. It is a term that still has an unclear definition of its lighting concepts.Nevertheless, it aims at achieving a profound paradigm shift in lighting, which after a technologically dominant phase, now increasingly defines the user needs as a central element of its developments. This process requires a significant redefinition of requirements of future lighting technologies.

Today, lighting products are designed for general visual needs and are not able to fulfill the diversity of individual needs. Future lighting systems must be configurable to the needs of single end users and thus have to provide easy-to-use interfaces to allow effortless adjustments of lighting control parameters to personal preferences and varying demands in different everyday situations. The acceptance rates of these lighting systems will be high as this will let the end user directly experience his own individuality.

Such product designs, which are tailored to individual needs, are not new to the lighting industry. For instance, switching lights on and off at any time can be understood as a rudimentary implementation of user needs. With dimmable room lights, visual preferences of individuals were focused at even a more extended level. Both, switching and dimming, instantaneously satisfies the needs of users and thus are highly accepted. These acceptance rates can hardly be reached with automatically controlled lighting systems.

The flaws of automation
Today, automated systems play a fundamental role for human-centric lighting. With the help of lighting control strategies, which automatically change the light intensity and light color depending on the time of day, healthy light dynamics can basically be implemented. But these lighting systems thwart the user-centricity of traditional user-controlled lighting systems by disregarding personal needs and preferences.

As a consequence, the need for justification of these automated lighting systems is increased as the end user is deprived of controlling rights which are now assigned to the light expert, who defines the not-adjustable lighting control strategy.

This shift of control from the end user to the light expert raises doubts as to whether automated lighting systems maintain user centered requirements in human-centric lighting because single user parameters are not utilized to define the lighting control curves.

In contrast, lighting control strategies refer to the general circadian rhythmicity in humans which is influenced by the 24-hour light/dark rhythm. Thus, it is assumed, that changing indoor light intensities and color temperatures according to a standard day, will support circadian entrainment by light and improve human health. Although research has clearly shown, that individuals endogenously differ in the period length of their circadian systems, up to now, only non-individualized, general lighting control curves are implemented in HCL solutions. This contradicts the basic idea of user-centered systems by ignoring the individuality of different users.

Two further aspects have to be considered: light stimuli for generating non-visual effects differ significantly between individuals and end-users show a strong idiosyncratic light control behavior.

The non-standardizable light stimulus for individuals
There is strong evidence that bright light acutely increases alertness, working memory and attention performance, and alters nocturnal melatonin and early morning cortisol levels [1]. In addition, light shifts the phase and changes the amplitude of circadian parameters [2]. These non-visual light effects can hardly be separated from visual information processes.

Furthermore, it is well documented, that past light exposure of the subject, circadian phase of the subject and spatial position of the subject when exposed to relevant light stimuli, play a crucial role in generating non-visual light effects.

Past light exposure of the individual
Individual light exposure of the past hours and days has a significant impact on regulating the sensitivity of the circadian system to light [3]. It is known, that regular light-dark changes over periods of 24 hours have a stabilizing effect on circadian rhythms [4] and protect against improper light stimuli (e.g., low light levels during the day and excessive light exposure at night).

Individually timed light exposure
Light exposure differently affects mood, cognition and behavior depending on the circadian phase of the individual [5][6]. Accordingly, timing of light exposure, especially with regard to the sleeping time, must be considered on an individual level.

The direct and indirect light input to individuals at different spatial positions
The spectral irradiance, measured at eye level, constitutes the light stimulus for generating non-visual light effects [7]. Light entering the eyes is basically composed of direct light of the luminaires as well as of indirect light, which is reflected by room surfaces. Furthermore, different spatial positions dramatically change the direct and indirect light input to humans. Thus, reflection properties of room surfaces, the emitted light intensity and light spectrum of the light sources and the position of the individual together play a crucial role in reaching certain spectral irradiances at eye level.

Taking these three parameters into account, light stimuli to achieve non-visual light effects prove to be highly variable between different subjects. Thus, it is essential to collect user-related information (e.g., light exposure history, sleep time, spatial position) and integrate this information into the light control strategy. However, as current HCL solutions are based on general pre-defined lighting control strategies, individual non-visual light needs are considerably ignored, and it can be assumed, that current HCL solution work sub-optimally.

The revolting subject — idiosyncratic light control behavior
In order to operate automatic lighting systems with maximum benefit, it is necessary to minimize user interventions. However, studies have shown that a subject’s comfort is negatively affected by a lack of control of environmental parameters [8]. Furthermore, the possibility of adjusting lighting to preferred levels greatly improves both, user satisfaction and system acceptance [9][10][11][12]. Therefore, operating fully automatic lighting systems run the risk of reducing user satisfaction.

In contrast, automatically controlled lighting systems which allow user interactions to a certain extent can be established. Because user interventions are typically based on actual needs, user behavior jeopardize the achievement of long-term (non-visual) light effects [13][14][15]. Thus, automatically controlled lighting systems, which lack information on user needs and preferences, are highly likely to encounter manual adjustment of pre-defined light settings. These adjustments often do not allow lighting systems to operate as intended and designed to [8]. In fact, the desire for individualization, alignment with personal preferences and possibility to control the lighting system is so high that users prefer personally controlled poorer lighting conditions over automatically controlled better lighting conditions [16].

This user behavior is confirmed by a three-year research project of Bartenbach (psylicht), in which the general psychiatric ward and the gerontopsychiatric ward of a Tyrolean hospital were equipped with state-of-the-art automated lighting solutions with user intervention options. Over a period of 24 hours, light intensities and color temperatures were changed automatically in all areas (patient rooms, bathrooms, corridors, staff rooms and recreational areas). In an iterative process the automatic changes in light settings were optimally adapted to working processes and social rhythms (e.g., sleeping and eating times and medical treatments of the patients) of the ward.

During the day, staff and patients were exposed to high brightness levels (up to 1000 lux at the eyes) under neutral white light (4000 K). In the evening and at night, bedrooms and bathrooms of patients were illuminated with reduced light intensities (< 50 lux) and the wards were lit with warm white light (2200 K). In addition, room lighting could be dimmed at any time and predefined lighting scenes (e.g., for television, eating) could be activated in recreational areas.

To measure user interventions with the lighting system as disruptive factors to generate non-visual light effects, all states of the lighting system (intensity and color temperature) as well as all user interventions, separated for each room area, were continuously recorded over a period of 18 months. Analyzing the data gave remarkable insights into the systematic individual lighting adjustment behavior.

For example, during the last hours of night shift work, where sleep pressure is high [17], the likelihood of changing light settings manually increased significantly (see Figure 1). Although, increasing light levels may counteract decreasing nighttime alertness levels [18], this light intervention has potentially negative effects on nocturnal melatonin levels, which indicates a disruption of the circadian system and may impair human health in the long term [19]. Although by decreasing light intensities and minimizing blue light components in ambient nighttime lighting detrimental light effects on circadian rhythms of night shift workers can be reduced [20], which was intended to be reached by the lighting designer, the instantaneous satisfaction of personal needs of end-users (i.e., improving nighttime alertness level by increased morning light) was observed.

Figure 1: Daytime-specific probability of user interventions during night shift in the staff room.

These experiences, gathered within a research project, establish user satisfaction and the end user’s desire to control lighting settings as a core element of future lighting control strategies. Systems that implement automatized 24-hour dynamics, based on pre-defined light settings, face a high probability to ignore basic needs of the user, which may cause manual interventions that significantly limit effective circadian lighting. In order to prevent such a user behavior, increase acceptance rates, and maximize the efficacy of the lighting system, it is necessary to integrate situational personal preferences into lighting control concepts. Further, data about personally adjusted light settings in the past provides input for currently used light control strategies, which leads to a sound balance between preferred and circadian effective light settings.

The urge for personalization
The integration of individual parameters into lighting control strategies exceed the capabilities of current automatic lighting systems. To create personalized lighting scenarios, providing information collected at user level to the system is a necessity. Although today’s HCL solutions are not ready for this information input, the implementation of connected lighting system lay the cornerstone for enabling direct communication between the lighting system, the environment and the user [21].

Thus, the implementation of personalized lighting system requires a fundamental and far-reaching change of thinking about future lighting control strategies. Rule-based control strategies, which are currently mainly implemented, are not able to meet the complexity of upcoming requirements of individual users. The use of model-based lighting control strategies [22], which are supported by artificial intelligence [23], may overcome these core limitations and establish lighting systems that are fully centered on the end-user and thus only pre-defined by lighting designers.

However, the implementation of fully user-centered lighting strategies in the long term requires that decision-making is handed over from the end user to a form of intelligence integrated into the lighting systems. Execution of this handover is extremely complex and algorithms for personalization are currently unavailable or barely applicable [13]. Personalized systems are therefore currently neither technologically nor conceptually available in the lighting industry.

Only when IP-to-the-end-node (luminaire) as well as standardized protocols for data exchange are delivered [24], fully personalized lighting control strategies without or with minimal manual intervention of end users are made possible. Finally, we expect that the development of such lighting systems will lead to a sustainable change of business models in the lighting industry. This assumption is corroborated by an end user survey carried out by Bartenbach in the course of the research project Repro-light. A total of 1096 end users in the application fields “office” and “industry” filled out a questionnaire to rate satisfaction levels with current workplace lighting systems and to specify core features of personalized lighting solutions.

One question within this survey especially focused on the desire for personalized lighting (“If you could have a workplace lighting system, that adapts to your personal needs, would you want it?”). Analysis of the ratings clearly showed that people like the idea of a personalized lighting system (Figure 2).

Figure 2: “If you could have a workplace lighting system, that adapts to your personal needs, would you want it?”.

Thus, personalized systems potentially do not only have a long-lasting positive impact on the viability of non-visual lighting effects, but they also point to potentially higher levels of acceptance among end-users. These can be particularly appreciated by end users with increased visual demands, as they are more open to new lighting ideas and technologies due to their higher expectations regarding their workplace lighting [25].

Conclusions
The integration of user-related information in lighting control strategies is proving indispensable to implement the concept of human-centric lighting. However, current HCL solutions are primarily realized as automations and handled as technical solutions for dynamically changing light intensities and color temperatures. The technological restrictions of these automated control concepts do not make it possible to consider user information sufficiently and are thus not able to differentiate user individualities. As a result, it has become apparent that the ability of current solutions to generate non-visual light effects may be severely limited as the variability and individuality of light needs of end users are ignored.

Further, the different ideas of system requirements of light experts and end users may have a detrimental effect on system efficiency, as the predefinition of fixed light curves for automation does not coincide with personal preferences or individual user requirements. This divergence may cause needs-driven user interventions which counteract planned system behaviors and thus significantly limit effective circadian lighting. It can currently be assumed that such interventions can be minimized by appropriately handling individual differences of users in lighting control solutions.

Thus, personalized lighting can not only contribute to higher acceptance rates and user satisfaction, but also maximize non-visual light effects. However, such systems are not yet available on the market and basic technological developments are still to be researched. According to the authors, especially the definition of open, interoperable protocols should be considered important, as they not only facilitate data exchange and interoperability but also enable complex model-based control strategies. In general, however, the basic idea of human-centric lighting is consistently proving to be correct and important, and thus remains unchanged. However, in order to no longer be subject to the conceptual and technical limitations of automated systems, the idea of HCL should be used to define requirements of technical implementations and the end user must be regarded technologically as an individual with personal qualities and opinions. Only then will it be possible to establish the user as a central element of control systems.

Acknowledgment
The research project psylicht was carried out with funding from the Austrian Research Promotion Agency. The project Repro-light has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768780.

Author: DI Johannes Weninger, Project Manager, Bartenbach GmbH, Rinner Strasse 14, 6071 Aldrans, Austria

Key Image: Johanna Buguet

References
[1]  Vandewalle, G., Maquet, P., Dijk, DJ. (2009). Light as a modulator of cognitive brain function. Trends Cogn Sci. 13(10): 429–38.
[2]  Khalsa, SB., Jewett, ME., Cajochen, C., Czeisler, CA. (2003). A phase response curve to single bright light pulses in human subjects. J Physiol. 549(Pt 3): 945–52.
[3]  Chang, AM., Scheer, FA., Czeisler, CA. (2011). The human circadian system adapts to prior history. J Physiol. 589(Pt 5): 1095–102.
[4]  Münch, M., Nowozin, C., Regente, J., Bes, F., De Zeeuw, J., Hädel, S., Wahnschaffe, A., Kunz, D. (2016). Blue-enriched morning light as a countermeasure to light at the wrong time: effects on cognition, sleepiness, sleep, and circadian phase. Neuropsychobiology 74(4): 207–218.
[5]  Fisk, AS., Tam, SK., Brown, LA., Vyazovskiy, VV., Bannermann, DM., Peirson, SN. (2018). Light and Cognition: Roles for circadian rhythms, sleep, and arousal. Front Neurol 9: 1–18.
[6]  Chang, AM., Santhi, N., St Hilaire, M., Gronfier, C., Bradstreet, DS., Duffy, JF., Lockley, SW., Kronauer, RE., Czeisler, CA. (2012). Human responses to bright light of different durations. J Physiol 590(13) 3103–12.
[7]  Lucas, RJ., Peirson, SN., Berson DM., Brown, TM., . . . , Brainard GC. (2014). Measuring and using light in the melanopsin age. Trends Neurosci. 37(1): 1–9.
[8]  Nagy, Z., Yong, F. Y., Schlueter, A. (2016). Occupant centered lighting control: a user study on balancing comfort, acceptance, and energy consumption. Energy and Buildings, 126, 310–322.
[9]  Fotios, S., Logadóttir, Á., Cheal, C., Christoffersen, J. (2012). Using adjustment to define preferred illuminances: do the results have any value?. Light & Engineering, 20(2), 46–55.
[10]  Despenic, M., Chraibi, S., Lashina, T., Rosemann, A. (2017). Lighting preference profiles of users in an open office environment. Building and Environment, 116, 89–107.
[11]  Gilani, S., O’Brien, W. (2017). Modelling and simulation of lighting use patterns in office spaces. Building Simulation, San Francisco, CA.
[12]  Escuyer, S., Fontoynont, M. (2001). Lighting controls: a field study of office workers’ reactions. Transactions of the Illuminating Engineering Society, 33(2), 77–94.
[13]  Fabi, V., Andersen, R. K., Corgnati, S. (2016). Accounting for the uncertainty related to building occupants with regards to visual comfort: A literature survey on drivers and models. Buildings, 6(1), 5.
[14]  Boyce, P. R., Veitch, J. A., Newsham, G. R., Jones, C. C., Heerwagen, J., Myer, M., Hunter, C. M. (2006). Occupant use of switching and dimming controls in offices. Lighting Research & Technology, 38(4), 358–376.
[15]  Lindelöf, D., Morel, N. (2006). A field investigation of the intermediate light switching by users. Energy and Buildings, 38(7), 790–801.
[16]  O’Brien, W., Gunay, H. B. (2014). The contextual factors contributing to occupants’ adaptive comfort behaviors in offices—A review and proposed modeling framework. Building and Environment, 77, 77–87.
[17]  Booker, LA., Magee, M., Rajaratnam, SM., Sletten, TL., Howard, ME. (2018). Individual vulnerability to insomnia, excessive sleepiness and shift work disorder amongst healthcare shift workers. A systematic review. Sleep Med Rev., available online 27 March, 2018.
[18]  Leppämäki, S., Partonen, T., Piiroinen, P., Haukka, J., Lönngvist, J. (2003). Timed bright-light exposure and complaints related to shift work among women. Scand J Work Environ Health 29(1): 22–26.
[19] Khan, S., Duan, P. Yao, L., Hou, H. (2018).
Shiftwork-mediated disruptions of circadian rhythms and sleep homeostasis cause serious health problems. Int J Genomics, available online 21 Jan, 2018.
[20] Canazei, M., Pohl, W., Bliem, HR., Weiss, EM. (2016). Acute effects of different light spectra on simulated night-shift work without circadian alignment. Chronobiol Int. 34(3): 303–317.
[21] Sabourin, N. T. (2017). The effects of connected lighting on lighting controls and design.
[22] Mittal, S., Frayman, F. (1989, August). Towards a Generic Model of Configuraton Tasks. In IJCAI (Vol. 89, pp. 1395–1401).
[23] Tiihonen, J., Felfernig, A. (2017). An introduction to personalization and mass customization. Journal of Intelligent Information Systems, 49(1), 1–7.
[24] Van de Werff, T., Van Essen, H., Eggen, B. (2017). The Impact of the Internet of Lighting on the Office Lighting Value Network. Journal of Industrial Information Integration.
[25] Repro-light, D2.2: Requirements Specification, 2018

(c) Luger Research e.U. – Institute for Innovation & Technology – 2018

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