Automatic detection of unbalanced sitting postures in wheelchairs using unlabeled sensor data

K Ramakrishna Kini, Fouzi Harrou, Muddu Madakyaru, Farid Kadri, Ying Sun

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This letter presents an effective data-driven anomaly detection scheme for automatically recognizing unbalanced sitting posture in a wheelchair using data from pressure sensors embedded in the wheelchair. Essentially, the designed approach merges the desirable features of the kernel principal components analysis (KPCA) as a feature extractor with the Kantorovich Distance (KD)-driven monitoring chart to detect abnormal sitting posture in a wheelchair. It is worth noting that this approach does not require labeled data and only employs normal events data to train the detection model, which makes it more appealing in practice. Specifically, we used the KPCA model to exploit its capacity to reduce the dimensionality of nonlinear data to obtain good detection. At the same time, the KD monitoring scheme is an efficient distribution-driven anomaly detection approach in multivariate data. Furthermore, a nonparametric decision threshold using kernel density estimation is adopted to extend the flexibility of the proposed approach. Publically available data has been used to verify the detection capacity of the proposed approach. The overall detection system proved promising, outperforming some commonly used monitoring methods.
Original languageEnglish (US)
Pages (from-to)1-4
Number of pages4
JournalIEEE Sensors Letters
StatePublished - Jul 22 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.


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