## 4 Results
#### Sensor Data Fusion  
Sensor fusion using DM tools helps extract, cluster features and merge data from both SSs. A block diagram of the sensor data fusion architecture employed in this study is presented in Figure 8 [37]. In Figure 8, data acquisition and preprocessing are performed by individual thermal sensors (T1 and T2). Up to 1000 features are extracted from the thermal (grayscale and binary) images. Thermal blobs gleaned from the ITA sensors are stored in a predetermined folder with timestamps to enable a time-based fusion of the data. During sensor fusion, data from T1 and T2 were imported into the data merging system. The system then created an imaginary table for the two sets of data before carrying out a matching row appending. Whilst file-import enables the reading of tabular data and their instances from an Excel spreadsheet or a text document, the image-import toolkit helps upload images from folders. Information such as image width, size, height, path, and name is automatically appended to each image uploaded in a tabular format. Preliminary feature extraction was programmed to begin automatically. To ensure that the features are correctly matched, a matching row appending was used. Moreover, definitive feature extraction takes place at a data embedding capsule where more than 1000 features, represented as vectors (n\({}_{0}\) to n\({}_{999}\)), are extracted from each ITA image. The extraction was performed by using the SqueezeNet architecture, a deep neural network model for image recognition [37]. The SqueezeNet architecture contains fewer parameters that require lesser bandwidth and communication across servers during a distributed training process [38]. Unlike many sensor fusion or classification architectures that manually allocate clusters to images, the Louvain clustering algorithm [37] was used alongside distance metrics to automatically detect clusters. One of the advantages of using Louvain clustering is that of determining the number of clusters detected. The Louvain clustering algorithm further detects and integrates communities into the module. It also converts grouped features into a KNN graph and optimises their structures to obtain nodes that are interconnected. Distance metrics, such as the cosine rule, were utilised in the Distances Application (DA). Additionally, feature normalisation, which performed column-wise normalisation for both categorical and numerical data, was applied [37]. The output of DA was connected to the hierarchical clustering module for the classification of the distanced features. Moreover, a dendrogram corresponding to a cluster of similar features from the DA was computed using the HCA. The clusters were primarily affected by resolution and Principal Component Analysis (PCA) parameters. In essence, increasing any of these parameters resulted in a corresponding decrease in the number of clusters that the algorithm detected. Data fusion outputs were viewed using a scatterplot, a data table, and a data viewer widget. Figure 8: Modified distributed sensor data fusion architecture for lateral (T1) and ceiling (T2) thermal sensors. One of the advantages of the sensor data fusion architecture proposed in this study includes viewing clusters comprising all similar activities, as presented in Figure 9, even if the activity was performed at different times by different participants. In Figure 9, for example, it could be easily deduced that a participant codenamed [PERSON]_ID was at the kitchen table with a hot cup of tea/coffee on the 8 May 2019 at a different date and time as another participant codenamed [PERSON]_OR. With this information, activities can be easily monitored in clusters, notwithstanding the times and dates they were performed. It is important to note that up to 1000 features (labelled n0 to n099) were extracted from each thermal image during the feature extraction process.