Abstract
We demonstrate an egocentric human activity assistant system that has been developed to aid people in doing explicitly encoded motion behavior, such as operating a home infusion pump in sequence. This system is based on a robust multi-camera egocentric human behavior detection approach. This approach detects individual actions in interesting hot regions by spatio-temporal mid-level features, which are built by spatial bag-of-words method in time sliding window. Using a specific infusion pump as a test case, our goal is to detect individual human actions in the operations of a home medical device to see whether the patient is correctly performing the required actions.
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Zhang, L., Gao, Y., Tong, W., Ding, G., Hauptmann, A. (2013). Multi-camera Egocentric Activity Detection for Personal Assistant. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_50
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DOI: https://doi.org/10.1007/978-3-642-35728-2_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35727-5
Online ISBN: 978-3-642-35728-2
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