## 2 Proposed Method  
The mapping of low-level sensor data into other forms, which may be more compact, more abstract, or more useful, involves various steps that go beyond the computational reasoning of the datasets. There are several questions that need to be addressed as a part of this process, including what types of data are needed, how the data will be stored, how the data will be processed, and how the results will be presented. In 1996, [PERSON] et al. described the knowledge discovery in databases process as the \"non-trivial process of identifying novel, potentially useful, and ultimately understandable patterns or relationships within a dataset in order to make important decisions\" [17]. So, KDD is a systematic and iterative way of uncovering structures of information, understandable patterns, from data that can be interpreted as valid. In addition, these entities should be valid for new data with some degree of certainty, resulting in some benefit to the end user or task [18]. As a result of this successful methodology proposed by [PERSON] et al., a number of different KDD approaches were developed, derived mainly for business uses [18]. The five steps (Sample, Explore, Modify, Model and Assess--SEMMA) constitute the data mining process developed by the SAS Institute for enterprises to solve different business problems [19]. Two Crows Consulting also proposed a data mining process model very similar to the original KDD process [20]. [PERSON] and [PERSON] proposed an internet-enabled knowledge discovery process model adapted to the web mining project [21]. Similarly, in 1997, [PERSON] et al. suggested a business-oriented KDD process that included most of the steps involved in the original KDD process [22]. [PERSON] and [PERSON] introduced an alternative perspective, a human-centred process, focusing on the data analyst as the key actor in the overall KDD process [23]. One of the main reasons for this argument was that the extraction of valuable knowledge requires prior background knowledge (i.e., an expertise) beyond the data and their analysis, and this background knowledge of the study area, according to the authors, resides only in the analyst. Depending on the KDD approach studied, the number of steps can vary; nonetheless, the generic steps involved in KDD are: (1) developing an understanding of the end goal, (2) collecting data, (3) selecting a target dataset, (4) cleaning and preprocessing data, (5) creating sub-sets of interest, (6) data mining, and ultimately, (7) producing outputs for evaluation (Figure 1). Traditionally, the KDD process has used data mining algorithms to automate the extraction of patterns. Generally, data mining techniques developed in the field of HAR in domestic environments have been classified into two main groups: data-driven and knowledge-driven approaches, as well as hybrid methods [7]. Regardless of the approach undertaken, the activity recognition process focusses on the creation of models that accurately map human activities. Reusability and scalability are the main challenges of these approaches, as the nature of human activities involves the sequencing of events, and a particular start time and duration for each step. Additionally, domestic environments vary in shape, form, and materials, and these factors influence, among other things, the way in which they are used by their inhabitants. Nonetheless, regardless of the computational method used, the results of the data mining step need to be presented in a meaningful way and in a form that can be dynamically adapted by an analyst through iterations so that conclusions can be drawn. Our KDD approach follows the idea put forward by [PERSON] and [PERSON]; we propose a human-centred approach that brings the analyst's background knowledge into the knowledge discovery process. The aim, therefore, is to make the background knowledge in the knowledge discovery process a key element in the elaboration of assumptions derived from the study of the sensor data.