Dynamic Model-Based Monitoring of Human Thermal Comfort for Real-Time and Adaptive Control Applications
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2018, Vol 19, Issue 4
Abstract
Thermal comfort and sensation are important aspects of the building design and indoor climate control as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that views the building occupants as passive recipients of their thermal environment. Assuming that people have relatively constant range of biological comfort requirements, and that the indoor environmental variables should be controlled to conform to that constant range. Recent advances in mobile technologies in healthcare, in particular wearable technologies (m-health) and smart clothing, have positively contributed to new possibilities in controlling and monitoring health conditions and human wellbeing in daily life applications. The wearable sensing technologies and their generated streaming data are providing a unique opportunity to understand the user’s behaviour and to predict future needs. Many advanced and accurate mechanistic thermoregulation models, such as the ‘Fiala thermal Physiology and Comfort’ model, are developed to assess the thermal strains and comfort status of humans. However, the most reliable mechanistic models are too complex to be implemented in realtime for monitoring and control applications. Additionally, such models are using not-easily or invasively measured variables (e.g., core temperatures and metabolic rate), which are often not practical and undesirable measurements for monitoring during varied activities over prolonged periods. The main goal of this paper is to develop dynamic model-based monitoring system of the occupant’s thermal state and their thermoregulation responses under two different activity levels. In total, 25 test subjects were subjected to three different environmental temperatures, namely 5° C (cold), 20° C (moderate) and 37° C (hot) at two different activity levels (at rest and cycling). Metabolic rate, heart rate, average skin temperature, skin heat flux and aural temperature are measured continuously during the course of the experiments. The results have shown that a reduced-ordered (second order)s MISO-DTF including three input variables (wearables), namely, aural temperature, heart rate, and average skin heat flux, is best to estimate the individual’s metabolic rate (non-wearable) with mean-absolute-percentageerror of 8.7%.
Authors and Affiliations
Ali Youssef, Nicolas Caballero, Jean Marie Aerts
Keywords
Related Articles
- EP ID EP622561
- DOI 10.26717/BJSTR.2019.19.003350
- Views 174
- Downloads 0
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