# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 1 Introduction
The idea behind adaptive models is that occupants and individuals are no longer regarded as passive recipients of the thermal environment but rather, play an active role in creating their own thermal preferences [18]. Many adaptive thermal comfort models are developed based on regression analysis (e.g., [18; 19; 20]). Besides regression analysis, thermal sensation prediction can also be seen as a classification problem where various classification algorithms can be implemented [17]. In their work [21], [PERSON] et al., proposed a method for learning personalized thermal preference profiles by formulating a combined classification and inference problem with 5-cluster models. Moreover, the thermal preference of a new user is inferred by a mixture of sub-models for each cluster, where clusters are used to group occupants with similar thermal preferences. Recently, a number of research works (e.g., [22; 23; 24; 25; 26] have demonstrated the possibility of using machine learning techniques, such as support vector machine (SVM), to assess and predict human thermal sensation. It can be concluded, based on the published work (see the recent literature review [17] by [PERSON] et al. ), that classification-based models have performed as well as regression models. Different related works investigated the problem of thermal sensation and comfort prediction via machine learning algorithms. [PERSON] et al. [27] applied the hidden Markov model (HMM) technique to the thermal comfort prediction problem with three levels of thermal comfort. There is a main issue in the used dataset in this study is the class imbalance, which is not tackled by the proposedmethodology. In their study, [PERSON] et al. did not discuss the problem of streaming analytics and model personalisation. In order to develop personalized models, [PERSON] et al. [28] applied support vector machines classifiers to the personal data of each subject to predict the thermal sensation level for the same subject. The obtained results are promising, however, their approach requires a sufficient number of data-points to obtain an acceptable performance, which is not applicable to our dataset (9 data-points per subject). The very recent study of [PERSON] et al. [17] proposed a personalized model, however, the study strictly investigated two subjects and developed a dedicated model for each subject. In comparison with many relevant studies, our study is tackling several challenges at the same time. These issues are feature reduction, streaming, and online modeling compatibility and model personalization. The latter issue is tackled in a novel way by considering both personal and nonpersonal data relying on the similarity either inter or intra subjects. In general, it can be stated that it is a real modeling challenge to correlate the physiological variables with information concerning global and local sensation [5]. 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 [29]. The generated streaming data is unique due to the personal nature of the wearable devices. However, the generated streaming data forms a challenge related to the need for personalized adaptive models that can handle newly arrived personal data. The main goal of this work is to introduce a personalized adaptive modeling algorithm to predict an individual's thermal sensation based on non-intrusive and easily measured variables, which could be obtained from already available wearable sensors.