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supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id caadria2020_313
authors Sanatani, Rohit Priyadarshi
year 2020
title A Machine-Learning driven Design Assistance Framework for the Affective Analysis of Spatial Enclosures
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 741-750
summary There is a growing research direction that adopts an empirical approach to affective response in space, and aims at generating bodies of quantitative data regarding the correlations between spatial features and emotional states. This paper demonstrates a machine-learning driven computational framework that draws upon training data sets to predict the 'affective impact' of designed enclosures. For demonstration, it has been scripted as a Rhinoceros + Grasshopper based design tool that uses existing training data collected by the author. The data comprises of the spatial parameters of Enclosure Volume (V), Length/Width ratio (P) and Window Area/Total Internal Surface Area ratio (D) - and the corresponding emotional parameters of Valence and Arousal. The test values of these parameters are computed by defining the components of the test enclosure (walls, windows, floors and ceilings) in the script. Nonlinear regression components are run on the training datasets and the test input data is used to compute and display the real time predicted affective state on the circumplex model of affect.
keywords Affective Analysis; Affective Computing; Design Assistance; Machine Learning; Spatial Enclosures
series CAADRIA
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