The assessment of occupants' wellbeing and productivity impact on building energy management is becoming a key topic in recent years due to increasing performance of the building stock still threaten by occupants' behavior variability. The paper aims to deeply investigate human perception in indoors which drives occupants' wellbeing and behavior through a novel measurement procedure, aimed at producing a multipurpose comfort perception scheme, i.e. considering thermal, visual, acoustic, and air quality comfort spheres. Data belonging to different domains of human perception are simultaneously measured: physical environmental parameters, physiological signals, and subjective responses. A preliminary series of measurement tests is here presented specifically focused on human response to thermal stimuli, i.e. subject exposed to increasing/decreasing temperature. Obtained data and are thus analyzed by coupling (i) physiological signals and subject responses through machine learning techniques, and (ii) personal attributes to sensation votes and environmental data variations. Results show potentials of the proposed measurement procedure which allows a comprehensive collection of physical attributes, physiological signals, and subjects’ psychological characterization. In conclusion, this work demonstrates the strict connection, with a prediction accuracy up to 84%, between physiological parameters (Heart Rate Variability and its indices) and human thermal comfort, opening the perspective of real-time measuring comfort for control and energy management purposes, taking into account human-centric parameters.

Assessing occupants’ personal attributes in relation to human perception of environmental comfort: Measurement procedure and data analysis

Arnesano, Marco;
2021

Abstract

The assessment of occupants' wellbeing and productivity impact on building energy management is becoming a key topic in recent years due to increasing performance of the building stock still threaten by occupants' behavior variability. The paper aims to deeply investigate human perception in indoors which drives occupants' wellbeing and behavior through a novel measurement procedure, aimed at producing a multipurpose comfort perception scheme, i.e. considering thermal, visual, acoustic, and air quality comfort spheres. Data belonging to different domains of human perception are simultaneously measured: physical environmental parameters, physiological signals, and subjective responses. A preliminary series of measurement tests is here presented specifically focused on human response to thermal stimuli, i.e. subject exposed to increasing/decreasing temperature. Obtained data and are thus analyzed by coupling (i) physiological signals and subject responses through machine learning techniques, and (ii) personal attributes to sensation votes and environmental data variations. Results show potentials of the proposed measurement procedure which allows a comprehensive collection of physical attributes, physiological signals, and subjects’ psychological characterization. In conclusion, this work demonstrates the strict connection, with a prediction accuracy up to 84%, between physiological parameters (Heart Rate Variability and its indices) and human thermal comfort, opening the perspective of real-time measuring comfort for control and energy management purposes, taking into account human-centric parameters.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/29989
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