This paper outlines the preprocessing methods and utilisation of clustering algorithms on a dataset [1] capturing individual tasks within a household (via energy consumption and reactive sensors). The analysis spans seven months that includes multi-sensor readings from a single household. In an effort to identify patterns through Human Activity Recognition (HAR), various clustering algorithms were applied to refined data to compare their respective outcomes. Hence, the paper examines multiple clustering algorithms suitable for the dataset exceeding 800,000 instances after preprocessing. It delves into the real-world applications of smart home data and conducts initial experiments where feasible, comparing results to uncover patterns indicative of user habits and changes therein. The study emphasises the potential for early intervention, particularly in identifying deviations to assist individuals such as those with dementia.