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MediSense: Bio-signal Data Filtering with Patient Motion Data

Recent advances in machine learning based data analytics are opening opportunities for designing effective clinical decision support systems (CDSS) which can become the “third-eye” in the current clinical procedures and diagnosis. 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, directly leading to potentially dangerous clinical decisions. We present MediSense, a system to sense, classify, and identify noise-causing motions and activities that affect physiological signal when made by patients on their hospital beds. Essentially, such a system can be considered as “glasses" for the clinical third eye in correctly observing medical data. MediSense combines wirelessly connected embedded platforms for motion detection with physiological signal data collected from patients to identify faulty physiological signal measurements and filters such noisy data from being used in CDSS training or testing datasets. We deploy our system in real intensive care units (ICUs), and evaluate its performance from real-patient traces collected at these ICUs through a 4-month pilot study at an urban university hospital trauma center. Our results show that MediSense successfully classifies patient motions on the bed with >90% accuracy, shows 100% reliability in determining the locations of beds within the ICU, and each bed-attached sensor achieves a lifetime of more than 33 days, which satisfies the application-level requirements suggested by our clinical partners.

Project Members: 
  • JaeYeon Park
  • Woojin Nam
  • Jaewon Choi
  • JeongGil Ko
  • Dukyong Yoon (School of Medicine)
  • Taeyeong Kim (School of Medicine)
  • Jeongyeop Paek (Chung-Ang University)


All software components used in this project is available through our Git Repository.


  • Jaeyeon Park, Woojin Nam, Tae Young Kim, Jaewon Choi, Sukhoon Lee, Dukyong Yoon, Jeongyeop Paek, JeongGil Ko. "Glasses For the Third Eye: Improving Clinical Data Analysis with Motion-sensor-based Filtering"ACM Conference on Embedded Networked Sensor Systems (ACM SenSys) 2017, Delft Netherlands.