Abstract
We investigate computer vision methods to monitor Intensive Care Units (ICU) and assist in sedation delivery and accident prevention. We propose the use of a Bed Aligned Map (BAM) to analyze the patient’s body. We use a depth camera to localize the bed, estimate its surface and divide it into 10 cm × 10 cm cells. Here, the BAM represents the average cell height over the mattress. This depth-based BAM is independent of illumination and bed positioning, improving the consistency between patients. This representation allow us to develop metrics to estimate bed occupancy, body localization, body agitation and sleeping position. Experiments with 23 subjects show an accuracy in 4-level agitation tests of 88% and 73% in supine and fetal positions respectively, while sleeping position was recognized with a 100% accuracy in a 4-class test.
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Martinez, M., Schauerte, B., Stiefelhagen, R. (2013). “BAM!” Depth-Based Body Analysis in Critical Care. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_56
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DOI: https://doi.org/10.1007/978-3-642-40261-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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