# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 3 Results and Discussions
### General Classification Models
#### 3.1.1 Developing General Model Using All Extracted Features for 7-Classes Problem (Model I)  
Initially, in this stage of developing a general classification model to predict thermal sensation, in total 54 features have been used to form the input space of the classification model for the 7-classes classification problem. The extracted features are meant to be simple and basic features that are not computationally expensive and represent the basic characteristics of segmented time windows. A feature space includes the mean value of the measured input variables, namely, \(T_{er}\), \(H_{R}\), \(\overline{q}_{sk}\), \(\Delta\overline{T}\) and \(M_{r}\). Additionally, other features are extracted by computing the variance, min, max, root mean squares (RMS), energy (\(E=\frac{1}{N}\sum_{n=1}^{N}x^{2}(n)\), where \(N\) is the number of samples of variable \(x\)) and first derivative (\(\frac{dx}{dt}\)) of the aforementioned measured variables as shown in Table 3. The age, gender, body-mass-index (BMI) and ambient temperature (\(T_{\infty}\)) are also included in the feature spaces. The output confusion matrix is computed for each subject based on LOSO testing approach. The averaged normalized confusion matrix over all test subjects is shown in Table 4 where the value of each cell \((i,j)\) represents the number of times (as percentage \"%\") that class \(j\) is classified as class \(i\). Given that the optimal situation is 100% for \(i=j\). From the resulted confusion matrix (Table 4) the overall accuracy of the developed classifier (Model I) is calculated to be 51%. In Table 4, there is the prediction result noted as 'Else', which represents the case that the classifier could not assign the test point to any of the presented classes. The error performance of the developed general model is depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{5-8} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Valance**} & **Mean** & **Min** & **Max** & **RMS** & **E** & \(\frac{d}{dt}\) \\ \hline \(T_{er}\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & - & \(\times\) \\ \(H_{R}\) & \(\times\) & \(\times\) & \(\times\) &  
1.2 Developing a General Model for 7-Classes Classification Problem with Dimension Reduction (Model II)  
As shown in Table 3, the input space of _Model I_ included all extracted features (54 features) that were obtained from the measured variables. However, for the sake of the main objective of the present work, the computational cost of the developed algorithm should be low enough to be compatible with wearable technology and online modeling. Hence, a feature selection procedure was employed to obtain the most reduced-dimension input space for the classification model yet with the best error performance. Feature selection here is based on evaluating all possible feature combinations and selecting the combination with best error performance. The used feature selection procedure resulted in a reduced input space of only 12 features with optimal feature combination. The selected features comprise: gender, age, \(H_{R}\), \(T_{er}\), \(T_{sk}\), \(\Delta\overline{T}\), \(\overline{q}_{sk}\), \(rms\) (\(H_{r}\), \(T_{c}\), \(T_{sk}\), \(\dot{q}\)), and \(\frac{d\overline{q}_{sk}}{dt}\) (time-derivative of average heat flux). The feature selection step reduced the input space from 54 features to only 12, which effectively reduced the computational costs of the classification algorithm during online implementation. The reduced dimension input space, including the selected 12 features, was used to develop a general classification model for the 7-classes classification problem to predict the thermal sensation of all test subjects.