# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 4 Discussion  
The main advantage of the proposed classification model in the present work, in comparison with other proposed models in recent studies (e.g., [17; 22; 25; 28]), is its capability to handle the requirements for adaptive personalization and online streaming modelling. Moreover, the proposed model is reduced-dimension, with the minimum possible number of features, which makes it computationally suitable for smart wearable technologies. The main results and findings of the present study is compared with recent studies that treat the prediction of the thermal sensation/comfort as a classification problem using machine-learning techniques. In their study [27], [PERSON] et al. used HMM classification technique, in which three classes of thermal comfort, namely, comfortable, uncomfortably cool and uncomfortably warm are used. An important point to be considered in the work of [PERSON] et al. [27] is the class imbalance in their used experimental data between the positive class (comfortable), which represents 81% of the data and the negative class (uncomfortable), which represents only 19% of the data. Therefore, using the classification accuracy (reported 82.8 %) is considered misleading in this case. Hence, it is much  
Figure 9: Flowchart represents the main workflow of the streaming data acquisition and labeling process. more suitable in their case to compare the precision and sensitivity of this model and our general model (Model III Conf. 3). The reported results [27] of [PERSON] et al. showed a precision of 93.3% and sensitivity of 56.22% without clarifying the precision and sensitivity of the uncomfortable states of warm and cool. On the other hand, our results of (Model III Conf. 3), which is the closest to the compared approach, show a precision of 88% for all classes and sensitivity of 88%, 91%, and 78% for Class 1, Class 2, and Class 3, respectively. These results show more balance between precision and sensitivity for each class. Moreover, personalization and streaming algorithm compatibility is missing in their study. Another relevant study [28], by [PERSON] et al., attempted to develop a personalized classification model, as for each subject, a classification model is trained with 50% of that subject data and tested with the rest. The reported result of this study [28] showed an average accuracy over all subjects of 89.82%. However, there is no clarification of the class distribution; hence, it is not clear whether the accuracy is efficient enough for evaluation. Moreover, it is not consistent to compare our final personalized model with that model as the latter is learned with seven classes; however, the former is learned with three classes. In another comparable study [25] to our present work, [PERSON] et al., predicted individual thermal comfort using machine learning classifier. In their study [PERSON] et al., used publicly available dataset from which a balanced number of each class is chosen to train and test the classification model. Their developed classification model is trained with three classes that represent the three thermal comfort states of uncomfortably cool, neutral and uncomfortably warm divided based on predefined comfort thresholds. In contrast to our proposed classification model, the proposed classifiers in [25] do not consider model personalization or streaming online modelling. The best-obtained results amongst their developed models are of the SVM classifier as follows: precisions of (76.92, 62.8, and 94.2%) and sensitivities of (67.5, 89.8, and 75.7%) for classes \(-1\), \(0\), and \(1\) respectively. On the other hand, our obtained results of (Model III conf. 3) are precisions of (88, 88, and 88%) and sensitivities of (88, 91, and 78%) of classes 1, 2, and 3 respectively. It is observed that the precisions of our developed classification model are more consistent for all classes, and the sensitivities are higher in total. Ultimately, their approach [25] does not consider personalizing the model or streaming online modelling.