Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models

TL;DR

This paper introduces a scalable method for collecting detailed longitudinal subjective comfort feedback via smartwatches, and develops classification models that predict occupant preferences based on environmental and physiological data.

Contribution

It presents a novel approach to gather intensive longitudinal comfort data and integrates it with sensor data to improve indoor comfort preference prediction models.

Findings

Achieved up to 86% F1 micro score in noise preference classification.

Collected 4,378 subjective surveys over two weeks from 30 occupants.

Demonstrated how combining subjective feedback with sensor data enhances comfort prediction.

Abstract

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.