Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods +
Footnote †: This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users' privacy, (ii) twearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test. [PERSON], 1,* 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 3  
(DM) and Machine Learning (ML) models, which can help discover patterns and potential deviations from established patterns in the data gleaned from a sensorised environment. Pattern Deviation Assessment (PDA) in activity recognition is a vital tool in detecting abnormal activities [4]. Its outcome helps to determine if an ageing individual can be considered to be independent or not whilst performing certain activities [4]. This is an important part of the home-based assessment process to gauge if a person can remain living in their own home. PDA can also help determine the extent of recovery from injury, potential hazardous behaviour, and an individual's effectiveness. Pattern deviation can take forms such as detecting incomplete activities and sudden changes in activity, disposition, and posture. PDA outcomes are often positioned in clusters to help access a set of activities or patterns on demand. The present work benefits from cluster-based analysis of patterns discovered from features extracted from thermal images using DM models and methods. Research in ARC has often considered the use of wearables such as accelerometers and video-based solutions such as Kinect [5; 6; 7; 8; 9; 10; 11]. Whilst accelerometers can provide information on orientation and angular acceleration of the worn part, wearability and data disruptions are some of the disadvantages. Likewise, Kinect has problems ranging from interference with external infrared sources to privacy and reflections in home environments [12; 13; 14; 15]. This work tackles these problems through the usage of Unobtrusive Sensing Solutions (USSs) such as Infrared Thermopile Array (ITA) thermal SSs, which are nonwearable and not prone to reflections in home environments. This article extends our previous research in [16] by conducting K-Means analysis on the fused, lateral, and ceiling sensor datasets using DM models. The extended work also includes the use of statistical tools such as an interval plot, a Two-Sample T-Test, and ANOVA to analyse average values of the models. Additional diagrams and annotations are also provided in the present work. The novel contributions of this work are four-fold. First, it presents an unobtrusive data collection through the use of non-wearable (i.e., privacy-friendly) USSs.