Abstract: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 label… Show more
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