Document Type : Research Paper
Authors
1 PhD candidate, of Architecture, Faculty of Architecture and Urban Planning, Imam Khomeini International University, Qazvin, Iran
2 Full Professor, Faculty of Architecture and Urban Planning, Imam Khomeini International University, Qazvin, Iran.
3 Associate Professor, Faculty of Architecture and Urban Planning, Shahid Rajaee Teacher Training University, Tehran, Iran
Abstract
Many studies have emphasized the use of daylight in the interior, due to its benefits and positive effects; one of the most significant of which is the users' various experiences of daylight-dependent factors. The present study aims to identify reliable methods, criteria, and tools to assess the users’ spatial experience (dependent variable) of daylight-dependent factors (independent variables). To this end, the qualitative method of structured review is applied to describe, analyze, and combine the existing authentic research published from 2012 to July, 2023. The findings reveal that the human experience of daylight can be divided into six physiological, neurophysiological, emotional, behavioral-motivational, cognitive, and visual groups, and researchers assess them through surveys, experiments, observations, case study or a combination of them. Self-assessment questionnaires are the most widely used tools in the survey method. However, assessing physiological and neurophysiological objective experiences has provided the opportunity to apply tools and sensors making it possible to integrate architecture with various fields of biomedical sciences, particularly neuroscience. In this method, the data obtained from the assessment of brain activities, heart status, skin, eye and head movements using sensors such as EEG, ECG, GSR, PPG, and eye and head tracking devices, are attributed to the person’s emotions and his acquisition of architectural experience. In the reviewed articles, researchers mostly tend to use technologies related to virtual reality and 360-degree rendering of images to show the stimuli to the subjects. Also, it seems that the use of machine learning and its algorithms to analyze the collected data, in order to create prediction models of human behaviors and spatial experiences in daylight studies, is expanding. Reviewing and analyzing today’s achievements and the existing platforms for future research, as the results of the present study, can ground extensive studies in this field.
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