Abstract
This research aimed to design a personal lighting cognition predict model based on machine learning techniques. As a pilot study, we will investigate the relationship between lighting conditions and physiological signals. The experiment involved four different illuminance levels: 30 lx, 100 lx, 200 lx, and 500 lx, under the slack correlated color temperature (CCT) of 3000 K. Fifty-five participants, ranging in age from young adults (20–40 years) to older adults (over 60 years), were recruited. For each illuminance state, 5 min of physiological data and lighting cognition questionnaire were collected. Our results showed that two feature extraction methods, one from the time domain and one from the frequency domain, led to different outcomes. Furthermore, specific features of physiological signals that can reflect subjective cognition under different lighting conditions.
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Hung, CL., Chou, CM. (2023). Lighting Cognition Predict Model From Physiological Signals - A Pilot Study. In: Rau, PL.P. (eds) Cross-Cultural Design. HCII 2023. Lecture Notes in Computer Science, vol 14023. Springer, Cham. https://doi.org/10.1007/978-3-031-35939-2_4
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DOI: https://doi.org/10.1007/978-3-031-35939-2_4
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