# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 5 Conclusions  
In this present paper, 25 participants are subjected to three different environmental temperatures, namely 5 \({}^{\circ}\)C (cold), 20 \({}^{\circ}\)C (moderate) and 37 \({}^{\circ}\)C (hot) at two different activity levels, namely, at low level (rest) and high level (cycling at 80 W power). Metabolic rate, heart rate, average skin temperature (from three different body locations), heat flux and aural temperature are measured continuously during the course of the experiments. The thermal sensation votes are collected from each test subject based on ASHRAE 7-points questioner. A general classification model based on LS-SVM technique is developed to predict the individual's thermal sensation. A localized learning algorithm based on KNN-LS-SVM approach is used to develop a personalized classification model to predict the individual's thermal sensation for 3-classes classification model. The developed classification model has the advantage of using a reduced-dimension input-space, which is suitable for wearable applications and onlinestreaming algorithm. The developed personalized model showed an overall accuracy result of 86%. Additionally, we introduced the main framework of streaming algorithm based on the developed personalized classification model to predict individual's thermal sensation based on streaming data obtained from wearable sensors. In the present work, we believe that it is the first time to utilize the localized learning approach in the thermal state classification problem. One of the main advantages of the proposed approach, in this paper, that it is suitable for streaming algorithm and online modelling as the computational cost is not influenced by increasing the number of data-points. However, the newly obtained data-points is to be considered to develop the online model, which is the main advantage of the KNN-LSSVM. Furthermore, the localized learning approach enables personalization of the classification model by considering either the personally labelled data-points or the most similar data-points of other persons. On the other hand, number of limitations, concerning the developed model, should be acknowledged here. One important limitation to the developed classification model is regarding to the data size, as the number of data-points per person and in total are generally limited. Moreover, the 7-classes labeling is unbalanced, which made the class reduction is necessary to enhance the overall prediction performance during the course of this study. Otherwise, this study would be extended to be applied to a 7-classes classification problem. The data balance and data size can be enhanced by asking for more frequent votes during the experiment and considering more than three environment temperature levels. Finally, another limitation regards the proposed KNN-LSSVM modeling approach, in which an extra hyperparameter (i.e., \(K\)) is to be optimized, which adds an extra computational cost to the overall streaming algorithm.  
Conceptualisation, A.Y., J.-M.A. and A.Y.A.A.; methodology, A.Y., A.Y.A.A. and N.C.; software, A.Y. and A.Y.A.A.; validation, A.Y. and A.Y.A.A.; formal analysis, A.Y., N.C. and A.Y.A.A.; investigation, A.Y., N.C. and A.Y.A.A.; resources, J.-M.A.; data curation, A.Y., N.C. and A.Y.A.A.; writing--original draft preparation, A.Y., N.C. and A.Y.A.A.; writing--review and editing, A.Y., J.-M.A. and A.Y.A.A.; visualisation, A.Y. and A.Y.A.A.; supervision, J.-M.A. and A.Y.; project administration, J.-M.A.; funding acquisition, J.-M.A.  
This research received no external funding.  
The Author [PERSON] is funded by a European Union Grant through the Interreg V-A Euregio Meuse-Rhine wearIT4 health project.  
The authors declare no conflict of interest.