KRnet 2004

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    [B3: IoT+Analytics] ÀÇ·á IoT ½Ã½ºÅÛ ±¸Ãà ¹× µ¥ÀÌÅÍ ºÐ¼®
    °ü¸®ÀÚ (krnet) ÀÛ¼ºÀÏ : 2017-05-18 12:44:08 Á¶È¸¼ö : 1101
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    ¹ßÇ¥ÀÚ¾à·Â : 2007 °í·Á´ëÇб³ ÄÄÇ»ÅÍÇаú °øÇлç
    2012 Johns Hopkins University Computer Science¹Ú»ç
    2012-2015 Çѱ¹ÀüÀÚÅë½Å¿¬±¸¿ø ¼±ÀÓ¿¬±¸¿ø
    2015-ÇöÀç ¾ÆÁÖ´ëÇб³ ¼ÒÇÁÆ®¿þ¾îÇаú Á¶±³¼ö
    2016-ÇöÀç ¾ÆÁÖ´ëÇб³ ÀÇ°ú´ëÇÐ ÀÇ·áÁ¤º¸Çаú °âÀÓ±³¼ö
    °­¿¬¿ä¾à : Recent advances in machine learning based data analytics are opening opportunities for designing effective clinical decision support systems (CDSS). However, common patient movements in hospital wards may lead to faulty measurements in physiological sensor readings, and training a CDSS from such noisy data can cause misleading predictions. We present MediSense, a system to sense, classify, and identify noise-causing motions and activities that affect physiological signals when made by patients. MediSense combines wirelessly connected embedded platforms for motion detection with physiological signal data collected from patients to identify and filter faulty physiological signal measurements. We deploy our MediSense in ICUs, and evaluate its performance using real-patient traces collected from a 4-month pilot study at the Ajou University Hospital Trauma Center.
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