## 2 Related Work  
Many SSs have been deployed over the years for the purposes of activity recognition [17; 18; 19]. These have included the use of wearable or non-wearable solutions or the fusion of both. Whilst they help data acquisition in the environment where they are deployed, their use in home settings can be negatively influenced by signals from other legacy systems and obstructive materials. Work in [20] proposed the use of a Hidden Markov Model to recognise human activities based on data gleaned from a waist-worn accelerometer. The model also classified collected signals according to a corresponding class. In the study, continuous monitoring was performed by a Gaussian Mixture Model. A further study by [PERSON] et al. [21] used a Multivariate Online Change Detection algorithm for activity recognition. Accelerometers for activity recognition have been featured in many studies [20; 22]. In [23], the use of triaxial accelerometers was proposed for monitoring rest, movement, transition, and emergency states in ageing adults. Although the successful detections of the activities were noted in the study, the ability to distinguish between activities and classify them accordingly was considered for further improvements. In [24], a triaxial accelerometer was used to monitor daily physical activity. In addition to the challenges of the approach presented in [23], wearability was an issue reported in the latter study. Another multi-wearable sensor study was carried out by [PERSON] et al. [22]. Whilst a garment-based accelerometer might exhibit improved performance in a laboratory environment, as illustrated by [22], real-life usage may suffer the risks of explosion or damage to the sensors during washing activity. Additionally, long term usage can cause a feeling of discomfort for the user. Activity Recognition and Classification (ARC) through the use of mobile devices has also been researched [16]. Work by [PERSON] et al. [7] explored the use of a smartphone's accelerometer to recognise and classify activities such as running and walking during a certain period of the day. The study obtained information from the GPS sensor to suggest to the user routines similar to those performed in previous days. The work presented by [25] suggested that mobile devices should be optimised to enhance the continuous monitoring and processing of data acquired from their sensors. Whilst these suggestions seem innovative and worthy of exploration, battery life and the users' ability to remember to carry mobile devices around are major setbacks. Furthermore, in [PERSON] et al. [26], a probabilistic examination of temporal and sequential aspects of activities using an approach based on the Cumulative Distribution Function is employed to determine abnormalities in activities. This approach involved deriving probabilities of normal behaviours with respect to the duration and the stages of an activity. Whilst this study introduced an effective way to detect (ab)normal activities, a profile analysis of users aimed at ensuring more precision in detecting the presence of health-related abnormalities is still being researched. Data fusion from homogeneous and heterogenous sensors has also been deployed in ARC. [PERSON] et al. [18] investigated the fusion of data from wearable (accelerometer) and ambient (thermal) sensors by extracting relevant features from both. Initial results from this approach indicated an improvement in abnormal behaviour detection. DM and ML models have positively influenced human activity recognition, clustering, and classification in home settings. In [27], the importance of cluster-based analysis of datasets is stressed beyond homogeneous to heterogeneous datasets, which are prevalent in real-world applications ranging from home-based to digital environments [28]. Work in [29] proposed the use of a Deep Neural Network (DNN) algorithm for home-based activity recognition. The study presented a fused DNN-based architecture that could predict the label of actions performed in a kitchen environment.