Automatic Detection of Unbalanced Sitting Postures in Wheelchairs Using Unlabeled Sensor Data

نویسندگان

چکیده

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 the wheelchair. Essentially, designed approach merges desirable features of kernel principal component analysis (KPCA) as feature extractor with Kantorovich distance (KD)-driven monitoring chart to detect abnormal It is worth noting that this does not require labeled but only employs normal event train model, which makes it more appealing practice. Specifically, we used KPCA model exploit its capacity reduce dimensionality nonlinear obtain good detection. At same time, KD efficient distribution-driven multivariate data. Furthermore, nonparametric decision threshold density estimation adopted extend flexibility proposed approach. Publicly available have been verify The overall system proved promising, outperforming some commonly methods.

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ژورنال

عنوان ژورنال: IEEE sensors letters

سال: 2022

ISSN: ['2475-1472']

DOI: https://doi.org/10.1109/lsens.2022.3193114