Glasses for the third eye: Improving the quality of clinical data analysis with motion sensor-based data filtering

Jaeyeon Park, Woojin Nam, Jaewon Choi, Taeyeong Kim, Dukyong Yoon, Sukhoon Lee, Jeongyeup Paek, Jeong Gil Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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. In this work, 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 the Ajou University Hospital Trauma Center, a major hospital facility located in Suwon, South Korea. 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. Furthermore, a simple case-study with arrhythmia patient data shows that MediSense can help improve the clinical diagnosis accuracy.

Original languageEnglish
Title of host publicationSenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
EditorsRasit Eskicioglu
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450354592
DOIs
Publication statusPublished - 2017 Nov 6
Event15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017 - Delft, Netherlands
Duration: 2017 Nov 62017 Nov 8

Publication series

NameSenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
Volume2017-January

Conference

Conference15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017
CountryNetherlands
CityDelft
Period17/11/617/11/8

Fingerprint

Hospital beds
Intensive care units
Decision support systems
Glass
Sensors
Learning systems
Testing

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Cite this

Park, J., Nam, W., Choi, J., Kim, T., Yoon, D., Lee, S., ... Ko, J. G. (2017). Glasses for the third eye: Improving the quality of clinical data analysis with motion sensor-based data filtering. In R. Eskicioglu (Ed.), SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems (SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems; Vol. 2017-January). Association for Computing Machinery, Inc. https://doi.org/10.1145/3131672.3131690
Park, Jaeyeon ; Nam, Woojin ; Choi, Jaewon ; Kim, Taeyeong ; Yoon, Dukyong ; Lee, Sukhoon ; Paek, Jeongyeup ; Ko, Jeong Gil. / Glasses for the third eye : Improving the quality of clinical data analysis with motion sensor-based data filtering. SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems. editor / Rasit Eskicioglu. Association for Computing Machinery, Inc, 2017. (SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems).
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Park, J, Nam, W, Choi, J, Kim, T, Yoon, D, Lee, S, Paek, J & Ko, JG 2017, Glasses for the third eye: Improving the quality of clinical data analysis with motion sensor-based data filtering. in R Eskicioglu (ed.), SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems. SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems, vol. 2017-January, Association for Computing Machinery, Inc, 15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017, Delft, Netherlands, 17/11/6. https://doi.org/10.1145/3131672.3131690

Glasses for the third eye : Improving the quality of clinical data analysis with motion sensor-based data filtering. / Park, Jaeyeon; Nam, Woojin; Choi, Jaewon; Kim, Taeyeong; Yoon, Dukyong; Lee, Sukhoon; Paek, Jeongyeup; Ko, Jeong Gil.

SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems. ed. / Rasit Eskicioglu. Association for Computing Machinery, Inc, 2017. (SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Park, Jaeyeon

AU - Nam, Woojin

AU - Choi, Jaewon

AU - Kim, Taeyeong

AU - Yoon, Dukyong

AU - Lee, Sukhoon

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Park J, Nam W, Choi J, Kim T, Yoon D, Lee S et al. Glasses for the third eye: Improving the quality of clinical data analysis with motion sensor-based data filtering. In Eskicioglu R, editor, SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc. 2017. (SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems). https://doi.org/10.1145/3131672.3131690