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. The (r)evolution in modern sensing and computing technologies (price, compact size, flexibility and stretchability) is making it possible to continuously measure signals in real-time from human body using wearable technologies and smart clothing. 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 real-time 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 5oC (cold), 20 oC (moderate) and 37 oC (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) 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 average mean-absolute-percentage-error of 8.7%. A general classification model based on least-squares-support-vector-machine (LS-SVM) technique is developed to predict the individual’s thermal sensation. For a 7-classes classification problem, the results have shown that the overall model accuracy of the developed classifier is 76% with a F1-score value of 84%.The developed thermal-state prediction model is promising to estimate the occupant’s thermal sensation/comfort status in real-time for better demand controlled HVAC systems.