Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 2:2022:8303856.
doi: 10.1155/2022/8303856. eCollection 2022.

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

Muhammad Usman Sarwar  1 , Labiba Fahad Gillani  1 , Ahmad Almadhor  2 , Manoj Shakya  3 , Usman Tariq  4
Affiliations

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

Muhammad Usman Sarwar et al. Comput Intell Neurosci. .

Retraction in

Abstract

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Block diagram of proposed approach.
Figure 2
Figure 2
Sample of raw and activity annotated sensor data. Sensors IDs starting with M are motion sensors.
Figure 3
Figure 3
Bar graph illustrating comparison results of OAR-CbC with the state-of-the-art study through F score on Aruba dataset. The range of the F score is between [0-1], with one being the highest. Key: OAR-CbC: proposed approach, APMTA [23], MkRENN [49], tlt: bed to toilet, eat: eating, EH: enter home, HK: housekeeping, LH: leave home, MP: meal preparation, Rlx: relax, Res: resperate, Slp: sleeping, WD: wash dishes, and WK: work.
See this image and copyright information in PMC

References

    1. Kumar P., Tripathi R., Gupta G. P. P2IDF: a privacy-preserving based intrusion detection framework for software defined Internet of Things-fog (SDIoT-Fog). Proceedings otin adjunct proceedings of the 2021 international conference on distributed computing and networking; January 2021; New York, NY, USA. pp. 37–42.
    1. Kumar P., Gupta G. P., Tripathi R. Design of anomaly-based intrusion detection system using fog computing for IoT network. Automatic Control and Computer Sciences . 2021;55(2):137–147.
    1. Kumar P., Kumar R., Srivastava G., et al. PPSF: a privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. IEEE Transactions on Network Science and Engineering . 2021;8(3):2326–2341.
    1. Lee Y., Rathore S., Park J. H., Park J. H. A blockchain-based smart home gateway architecture for preventing data forgery. Human-centric Computing and Information Sciences . 2020;10(1):1–14.
    1. He S., Zeng W., Xie K., Yang H., Lai M., Su X. PPNC: privacy preserving scheme for random linear network coding in smart grid. KSII Transactions on Internet and Information Systems (TIIS) . 2017;11(3):1510–1532.

Publication types

LinkOut - more resources