  Evaluating and optimising human comfort within the built environment is
challenging due to the large number of physiological, psychological and
environmental variables that affect occupant comfort preference. Human
perception could be helpful to capture these disparate phenomena and
interpreting their impact; the challenge is collecting spatially and temporally
diverse subjective feedback in a scalable way. This paper presents a
methodology to collect intensive longitudinal subjective feedback of
comfort-based preference using micro ecological momentary assessments on a
smartwatch platform. An experiment with 30 occupants over two weeks produced
4,378 field-based surveys for thermal, noise, and acoustic preference. The
occupants and the spaces in which they left feedback were then clustered
according to these preference tendencies. These groups were used to create
different feature sets with combinations of environmental and physiological
variables, for use in a multi-class classification task. These classification
models were trained on a feature set that was developed from time-series
attributes, environmental and near-body sensors, heart rate, and the historical
preferences of both the individual and the comfort group assigned. The most
accurate model had multi-class classification F1 micro scores of 64%, 80% and
86% for thermal, light, and noise preference, respectively. The discussion
outlines how these models can enhance comfort preference prediction when
supplementing data from installed sensors. The approach presented prompts
reflection on how the building analysis community evaluates, controls, and
designs indoor environments through balancing the measurement of variables with
strategically asking for occupant preferences in an intensive longitudinal way.
