## 4 Results
#### Sensor Data Fusion
Using these features, a PCA and scoring of the clusters performed between features n525 and n830 at 99% variance coverage indicated a regression coefficient (r) of 0.98 and 1.00 for Clusters 2 and 12, respectively, as presented in Figure 10.  
Similarly, a PCA and scoring analysis performed between features n246 and n170 for Clusters 1, 6, and 9 yielded (r) of 0.83, 0.99 and 1.00, respectively, as presented in Figure 11. These resulted in an average (r) of 0.95 for all the tested features and clusters, which were randomly selected from the HCA interface. To further ascertain the certainty of the predicted clusters, an Evaluation Test was performed on all the clusters in the HCA using the KNN, LR, NN, and RF models. While KNN yielded the lowest CA of 85.0%, LR and NN gave CAs of 96.1% and 100.0%, respectively, as presented in Table 1. In addition, the proportion of true positives of the positively classified instances (Precision) followed a similar trend as the CA. Furthermore, the NN yielded a value of 100.0% for the AUC, F1, CA, Precision, Recall, and Specificity followed by RF with an average of 99.7%, as presented in Table 1.  
Figure 9: A cluster of data fusion output showing thermal blobs from two participants in a cluster with timestamps. The black arrow on “C_ID_080519_11.58”thermal_212.png’ points to the location of the lateral sensor, the red arrow points to the participant, and the white arrow points to the heat spot from tea/coffee cups. Figure 11: Features-based Principal Component Analysis and scoring of clusters. Features n170 and n246 are indicated on the X- and Y-axes, respectively. Additionally, the clusters are colour coded, and the colour of each regression line on the graph matches the colour on the cluster legend on the right. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **AUC (\%)** & **CA (\%)** & **F1 (\%)** & **Precision (\%)** & **Recall (\%)** & **LogLoss (\%)** & **Specificity (\%)** \\ \hline KNN & 99.1 & 85.0 & 85.0 & 85.4 & 85.0 & 0.3 & 98.3 \\ LR & 99.9 & 96.1 & 96.1 & 96.1 & 96.1 & 0.2 & 99.6 \\ NN & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 0.0 & 100.0 \\ RF & 100.0 & 98.9 & 98.9 & 98.9 & 98.9 & 0.3 & 99.9 \\ Average & 99.7 & 95.0 & 95.0 & 95.1 & 95.0 & 0.2 & 99.4 \\ \hline \hline \end{tabular} Legend: KNN = K-Nearest Neighbours, LR = Logistic Regression, NN = Neural Network, RF = Random Forest, CA = Classification Accuracy, and AUC = Area Under the Curve.  
\end{table} 
Table 1: Evaluation results from data mining models for parameters such as AUC, CA, FI, Precision, Recall, LogLoss, and Specificity. Figure 10: Features-based Principal Component Analysis and scoring of clusters. Features n525 and n830 are indicated on the X- and Y-axes, respectively. The clusters are colour coded, and the colour of each regression line on the graph matches the colour on the cluster legend on the right. LogLoss, also referred to as cross-entropy loss, accounts for the performance of the classification model with respect to its variation from the actual label and was relatively low (less than 0.4%) for all the models (Table 1). NN had the most negligible value of 0.001%. While an average regression coefficient of 0.95 was obtained in the PCA and scoring test, an average accuracy of 96.5% was obtained for all the metrics (in Table 1) in the Evaluation Test. Another demonstration of the accuracy of the architecture was in the analysis of the ceiling and lateral thermal sensors data using the K-Means Clustering Method (KMCM). The KMCM is rated as a useful tool capable of providing quantitative and qualitative insight in multivariate analysis [39]. The data fusion and evaluation architecture based on the KMCM [40], is presented in Figure 12.  
The KMCM-based architecture (Figure 12) fused thermal blobs data from thermal sensors T1 and T2. The fusion toolkit was linked directly to the image embedder. At the embedder, Inception V3, Google's ImageNet trained model [41] was used to embed the thermal blobs.