## 3 Materials and Methods  
Research in human activity recognition is an important monitoring process in smart homes [31] that has witnessed the use of wearable and non-wearable SSs. In this study, attention was given to privacy-friendly USSs. Additionally, the study was carried out in a smart laboratory kitchen that mimics a typical home kitchen [34]. More than 11,000 thermal blobs were recorded from 10 participants with two Infrared Thermopile Array (ITA) sensors. Participants were asked to prepare either a cup of tea or coffee. The present work uses two ITA-32 sensors to monitor and recognise activities in a laboratory kitchen, which is similar to a home kitchen. The two thermal sensors are used simultaneously to address instances of missing thermal blobs due to occlusion. Automated processing techniques are used to synchronise and extract features and to fuse data from both sensors. Contact sensors are used as the baseline to compare their timestamps with those of thermal sensors. The study was carried out in a laboratory kitchen (Figure 1), which measures 3.9 m by 3.4 m. Ten healthy participants took part in the study, and each of them participated in a total of seven experiments. To have a more realistic scenario, participants were allowed to take as long as they wished to complete the activities in each experiment. There were no time constraints or control on the duration of the activities undertaken. The laboratory kitchen is composed of cupboards (labelled 1-4 in Figure 2) where tea, coffee, cups, and sugar were stored. Underneath the cupboards is a worktop with a microwave, a kettle, and a sink, thus mimicking a real-life kitchen. A refrigerator is located on the floor beneath the worktop, as indicated in Figure 2. The main kitchen area is where participants walked around to prepare a hot beverage (either tea or coffee), which was then taken to the table area for consumption. In Figure 2, the lateral and the ceiling SSs are represented as T1 and T2, respectively. Whilst T1's indicative coverage included half of the kitchen area, as represented by the triangular shades in Figure 2, T2's coverage included a larger portion of the kitchen area, as indicated by the oval shades. During data acquisition, each participant (at a time) walked in through door D1 to the main kitchen area where the cups were located. While some participants preferred to boil water in the kettle before going for the cups, others did the opposite. Data acquisition began a few seconds prior to opening door D1, notwithstanding the activity preferences of the participants. Figure 1: Pictorial view of the smart laboratory kitchen used for the study. A detailed description of the kitchen layout is presented in Figure 2.  
Data from T1 and T2 were stored in a bespoke time-series database referred to as SensorCentral [35; 36]. A total of 11,980 frame data (1198 from each participant) were collected from the seven experiments. The contact sensors, which were also associated with the database, were able to record the times when each activity began and ended. Moreover, contact sensors were used as the baseline to compare the timestamps of both types of sensors. They also help to indicate which of the participants had tea or coffee. DM tools and algorithms were used to extract features and to fuse data from both sensors. The DM algorithms used included the Hierarchical Clustering Algorithm (HCA) and the K-Means Algorithm (KMA). Metrics such as Classification Accuracy (CA), Specificity, weighted average (F1), Recall, and Area Under the Curve (AUC) were used the ascertain the performance of DM models such as K-Near Neighbours (KNN), Logistic Regression (LR), and Neural Network (NN). Others included Random Forest (RF), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM). Figure 2: Laboratory kitchen layout. The areas marked in red indicate the location of the contact sensors. Thermal sensors are indicated by the navy-blue oval shape as T1 and T2 for lateral and ceiling thermal sensors, respectively.