Investigating the biomechanical impact of lighting placement on visual and physical comfort in living room interior design
Abstract
Lighting is a key factor in shaping comfort, ambiance, and functionality within residential spaces, influencing not only visibility but also a room’s overall experience and usability. In living rooms, where activities range from socializing and relaxation to reading and television viewing, lighting design must balance visual clarity, warmth, and adaptability to meet diverse needs. This study investigates the effects of specific lighting placements—overhead, wall-mounted, and floor/table lamp setups—across warm (2700 K), neutral (4000 K), and cool (6500 K) color temperatures on visual and physical comfort in a simulated residential living room environment. Using a mixed-methods approach, quantitative metrics, such as brightness consistency and luminance contrast, were combined with qualitative assessments of perceived comfort and activity suitability. Findings reveal that warm-toned floor and table lamps (2700 K) provide the highest levels of perceived warmth and relaxation, with average ratings of 4.9 and 4.8, making them particularly suitable for social and leisure activities. Overhead lighting in cool tones (6500 K) enhanced visual clarity, achieving an average clarity rating of 4.5, making it more suited to tasks requiring focused attention, such as reading. Wall-mounted lighting in neutral tones (4000 K) offered a balanced solution, with comfort and activity suitability ratings of 4.5, supporting a range of activities without compromising ambiance or clarity. These results underscore the importance of selecting lighting configurations that align with the intended use of residential spaces. Warm lighting, especially at lower levels, creates a cozy, inviting atmosphere most effectively, while cooler overhead lighting offers enhanced brightness and clarity for more visually demanding tasks. By highlighting the impact of lighting placement and color temperature on residential comfort, this study provides practical insights for interior designers and homeowners, contributing to developing adaptable, user-centered lighting solutions that optimize functionality and ambiance in home environments.
References
1. Ozenen, G. (2023). Lighting Fundamentals and Design Principles. In Architectural Interior Lighting (pp. 15-24). Cham: Springer Nature Switzerland.
2. Akinbami, A. A. (2024). Integrating Natural Light for Wellbeing, Performance, and Quality Care Delivery in Healthcare Environments.
3. Steffen, R. (2023). Exploring effects of lighting in physical and virtual spaces (Doctoral dissertation, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau).
4. Sumartojo, S. (Ed.). (2022). Lighting design in shared public spaces. London, UK: Routledge.
5. Ozenen, G. (2024). Architectural interior lighting. Springer.
6. Füchtenhans, M., Grosse, E. H., & Glock, C. H. (2021). Smart lighting systems: state-of-the-art and potential applications in warehouse order picking. International Journal of Production Research, 59(12), 3817-3839.
7. Al-Saigh, M. N., & Mahmoud, K. F. (2023). The Impact of Smart Interactive Technologies in Creating Personal Internal Spaces: An Analytical Study of User Preferences for Interactive Shape Characteristics. International Journal of Sustainable Development & Planning, 18(8).
8. Konstantzos, I., Sadeghi, S. A., Kim, M., Xiong, J., & Tzempelikos, A. (2020). The effect of lighting environment on task performance in buildings–A review. Energy and Buildings, 226, 110394.
9. Casciani, D. (2020). Lighting Design and New Lighting Technologies for Enhanced Learning Environments. In INTED2020 Proceedings (pp. 3418-3427). IATED.
10. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.
11. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.
12. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.
13. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.
14. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.
15. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.
16. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.
17. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.
18. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors, and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030.
19. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.
20. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563-574, https://doi.org/10.53759/7669/jmc202404054.
21. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x
22. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.
23. Ahakmi, P., & Pourmokhtar, A. Investigating the Effect of Flexibility of Residential Spaces on Strengthening the Comfort of Residents.
24. Olajiga, O. K., Ani, E. C., Sikhakane, Z. Q., & Olatunde, T. M. (2024). A comprehensive review of energy-efficient lighting technologies and trends. Engineering Science & Technology Journal, 5(3), 1097-1111.
25. Li, S. (2022). The integration of hotel interactive lighting systems to support users' visual comfort and activity needs.
26. Fotios, S., Houser, K., & Cheal, C. (2015). Perceptions of visual discomfort from lighting: Impact of lamp spectrum. Lighting Research & Technology, 47(3), 270-289.
27. Wang, X., Zhang, L., & Li, Y. (2021). Lighting design and control for human health and well-being in indoor environments—Journal of Building Engineering, 43, 102885.
28. Borisuit, A., Linhart, F., Scartezzini, J. L., & Münch, M. (2015). Effects of realistic office daylighting and electric lighting conditions on visual comfort, alertness, and mood. Lighting Research & Technology, 47(2), 192-209.
29. Fotios, S., & Gado, M. (2020). A review of experimental methods used to investigate glare. Building and Environment, 168, 106504.
30. Hwang, T., & Kim, J. (2018). User experience and comfort based on luminous environment of residential space. Sustainability, 10(9), 3352.
31. Rea, M. S., & Figueiro, M. G. (2022). Light–much more than vision: Effects of circadian light on health and well-being. Annual Review of Vision Science, 8, 101-117.
32. Spitschan, M., Stefani, O., & Blume, C. (2022). Human-centric lighting: A critical review of the evidence. Annual Review of Public Health, 43, 283-306.
Copyright (c) 2024 Yabin Chen, Qiong Wu, Shijia Wang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.