Towards Adaptive, human-centric lighting: Limitations of Subjective Measures and a multimodal framework

Welsh School of Architecture, Cardiff University, CF10 3NB, United Kingdom

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Abstract

Indoor lighting plays a crucial role in shaping occupant comfort, health, and productivity, as they spend 90% of their time indoors. While intensive research on adaptive lighting technologies is ongoing, most current strategies are still primarily based on static standards and subjective questionnaires, which may not reliably capture dynamic human responses to real-time changes in lighting conditions. Also, current strategies often address only a few parameters, such as illuminance and colour temperature, and overlook how variations in lighting influence actual human behaviour. This paper presents a comprehensive review of the literature spanning the non-visual effects of light. It highlights persistent methodological shortcomings, i) particularly the narrow parameter focus, ii) methodological constraints like reliance on invasive sensing, static comfort models, and laboratory-based studies with limited ecological validity and iii) difficulty with integration with control systems. The paper further examines the limitations of relying solely on subjective lighting evaluations for occupant-responsive lighting control. A living lab study was conducted in a university environment (n = 81), where illuminance levels were measured across nine spatial zones and compared with occupants' self-reported satisfaction, perceived control, and productivity using Likert-scale surveys. Results showed weak and inconsistent relationships between measured illuminance and subjective responses. The zone with the highest illuminance also reported the highest dissatisfaction, indicating a mismatch between objective lighting levels and perceived comfort. Response clustering, adaptation effects, and contextual confounding factors further limited the sensitivity and reliability of subjective assessments. The findings highlight the importance of integrating non-invasive behavioural and physiological sensing modalities with qualitative assessments when evaluating indoor lighting environments. Therefore, the paper proposes a structured methodological framework that employs non-invasive behavioural and physiological drivers, such as eye-tracking metrics, heart rate and heart-rate variability, facial expression analysis, and posture and movement patterns to capture real-time occupant responses under static and dynamic lighting conditions. The framework is designed to support transparent, closed-loop lighting adaptation while maintaining user override and practical deployability.