Lee, J and Ham, Y (2021) Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations. Building Research & Information, 49(5), pp. 512-524. ISSN 0961-3218
Abstract
As one of the representative parameters for human energy metabolism, the metabolic rate has been considered as the significant factor for occupants' thermal comfort analyses. Despite the importance of metabolic rate as a predictor of thermal comfort modelling, prior works rely on uncertain metabolic rate estimation without considering actual activity variations while occupying a building. This study aims at identifying the effect of metabolic rate on the thermal comfort models by proposing a robust data-driven personalized model in consideration of human activity variations. To investigate heterogeneous thermal state of occupants, wearable sensors and machine learning algorithms were used to continuously monitor and analyse individual physiological signals, activity-based metabolic rates and environmental indices. Field experiments were conducted with 10 subjects in a campus building in the US, and the results showed that predictive models considering metabolic rate yield advanced performance of up to 8.5%, implying that activity-based metabolic rates provide better understanding of personal thermal comfort. This paper quantitatively validates the effectiveness of reflecting metabolic rate based on human activity variations into personal thermal comfort modelling, which provides an insight into how to better model personal thermal comfort of occupants in real-life settings.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | buildings; data collection; information and communication technologies; thermal comfort |
| Index terms: | modelling, wearable sensor, estimation, machine learning, variation, information and communication technology, experiment, effectiveness, human activity, thermal comfort, human energy |
| Subjects: | sociology, environmental engineering, contractual condition, data collection methods, computing systems, performance management, analytical methods, medical science and clinical practice, computer vision, financial and cost management, artificial intelligence |
| Topics: | Health and Safety, Quality Management, Contract Administration, Cost Management, Digital Applications, Research Practice, Sustainability, Engineering Principles |
| Descriptive scope: | 3 PCE |
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