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Abstract
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. Timestamps 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. 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 language | English |
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Journal | Applied Sciences |
DOIs | |
Publication status | Published - 29 Sept 2021 |
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Dive into the research topics of 'Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification using Data Mining Models and Methods'. Together they form a unique fingerprint.Projects
- 1 Finished
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EnnCore: End-to-End Conceptual Guarding of Neural Architectures
Cordeiro, L. (PI), Brown, G. (CoI), Freitas, A. (CoI), Luján, M. (CoI) & Mustafa, M. (CoI)
1/02/21 → 31/12/24
Project: Research