## 5 Discussion and Conclusions  
This study presented the fusion of data gleaned from USSs for the purposes of recognising and classifying indoor activities in home environments. It considered the use of DM models and methods for the cluster-based analysis of data obtained from the USSs. Results from data analysis demonstrated a pooled Mean accuracy of 96.5% for all the models and metrics considered in the study. Although the Mean accuracy in F1 data was slightly lower than in T1, a one-way ANOVA of the samples, T1, T2, and the F1 datasets indicated no significant difference between their Mean values. In addition, data fusion provided more information on instances of occlusion, which can make an incident go unnoticed. The advantage of the proposed method in this work over other indoor activity recognition research [33; 43] includes privacy-friendly postures and better accuracy. The accuracies obtained in this work can be compared with those obtained in [44], which used channel state information of a WiFi system to recognise activities such as lying down, standing, and walking. While the WiFi-based system has no information on the postural orientation of participants or the presence of hazardous objects, our model included privacy-friendly postures. Knowledge of the pose of room occupants and the surrounding objects can give further details, such as hot liquid spills, which can be hazardous to vulnerable individuals. The application of this study to smart homes and healthcare facilities can help encourage independent living [45; 46; 47]. One of the limitations of this study is the use of the contact sensors to determine if an occupant drank tea or coffee during the experiments since both (tea and coffee) were placed in the same cupboard. This implies that depending on the data from the thermal sensors alone, it would be difficult to determine if an occupant had tea or coffee. In a real-life \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Sources** & **Data Points (N)** & **Mean Accuracy \%** & **StdDev** & **95\% CI** \\ \hline Lateral & 4 & 98.0 & 0.8 & (96.1, 99.8) \\ Ceiling & 4 & 95.0 & 2.2 & (93.2, 96.9) \\ Fusion & 4 & 96.5 & 1.5 & (94.6, 98.4) \\ Averages & 4 & 96.5 & 1.5 & (94.6, 98.4) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed analyses of Mean values from Lateral, Ceiling, and Fused datasets using one-way ANOVA. Figure 13: Interval plot of Lateral, Ceiling, and Fused datasets computed from their pooled standard deviation. setting, however, this confusion could be resolved if tea and coffee are placed on separate cupboards that are more than 1 m apart. Another challenge with using the thermal sensors only without the contact sensors is determining if the occupant used milk or cold water if both are placed in a similar container. To address this limitation in a real-life application, milk and cold water should be placed in containers of different sizes so that their blobs could be easily differentiated. In conclusion, this study presented the use of low-cost unobtrusive (privacy-friendly) SSs for indoor ARC in a laboratory kitchen environment similar to a home environment. Experimental results indicated instances of activity recognition during activities such as making a cup of tea/coffee and classification of the same actions using DM models and methods with a pooled Mean predictive accuracy of 96.5%. Future studies will calculate the speed and range of these activities, including the use of DM tools to score and evaluate their performance. Conceptualization, I.E. and C.N. ; methodology, I.E. ; software, I.E. ; validation, M.G.-C. and C.N. ; formal analysis, I.E. ; investigation, I.E. and M.G.-C.; resources, C.N. ; data curation, I.E. and M.G.-C.; writing--original draft preparation, I.E. ; writing--review and editing, M.A.M., Y.D.-S., A.K. and M.G.-C.; visualization, M.G.-C.; supervision, C.N., and J.M. ; project administration, C.N. ; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript. Funding: EU's INTEREG VA program, IVA5034.