Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification.

Autor: Sarwar MU; Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan., Gillani LF; Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan., Almadhor A; College of Computer and Information Sciences, Al Jouf University, Sakakah, Saudi Arabia., Shakya M; Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal., Tariq U; College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 02; Vol. 2022, pp. 8303856. Date of Electronic Publication: 2022 Jun 02 (Print Publication: 2022).
DOI: 10.1155/2022/8303856
Abstrakt: The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities. The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities). The proposed approach overlapping activity recognition using cluster-based classification (OAR-CbC) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level. Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level. We examine four partitioning and classification techniques with the same hierarchy for a fair comparison. The OAR-CbC evaluates on smart home datasets Aruba and Milan using threefold and leave-one-day-out cross-validation. We used evaluation metrics: precision, recall, F score, accuracy, and confusion matrices to ensure the model's reliability. The OAR-CbC shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Muhammad Usman Sarwar et al.)
Databáze: MEDLINE
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