Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification using Data Mining Models and Methods

Idongesit Ekerete, Matias Garcia-Constantino, Alexandros Konios, Mustafa Mustafa, Yohanca Diaz, Christopher Nugent, James McLaughlin

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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) wearability, (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.
Original languageEnglish
Article numberapplsci-1390601)
Pages (from-to)1-17
Number of pages17
JournalApplied Sciences
Issue number19
Publication statusPublished (in print/issue) - 29 Sept 2021

Bibliographical note

Funding Information:
Acknowledgements: Research is funded by the EU’s INTERREG VA program, managed by the Special EU Program Body (SEUPB).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • K-Means analysis
  • home environment
  • Sensor fusion
  • Activity recognition
  • Data mining
  • Unobtrusive sensing
  • infrared thermopile array
  • Principal components analysis
  • K‐Means analysis
  • Infrared thermopile array
  • Home environment
  • Principal component analysis


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