For the past couple of months, I’ve been engaged in development of a machine learning driven design assistance framework for the affective analysis of spatial enclosures. The framework relies on data pertaining to emotional response patterns in spatial enclosures which can collected through focused experiments, and uses this data for predictive affective  modeling for realtime design assistance.

Some of my initial progress in this direction was recently presented at the 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2020).



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.

Download the full paper here

CAADRIA 2020: Machine Learning for Affective Analysis

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