skip to main content
10.1145/3321335.3329679acmotherconferencesArticle/Chapter ViewAbstractPublication PagesperdisConference Proceedingsconference-collections
demonstration

Integration of multimodal alarms into Google Glass: demo

Published:12 June 2019Publication History

ABSTRACT

Nurses in intensive care units (ICU) are often exposed to noise pollution, especially a high number of acoustic patient alarms. One widespread consequence of these alarms is a condition called alarm fatigue. This means a desensitization and a resulting decreasing response time to alarms. To reduce the number of alarms, many interventions have been explored. However, the remaining acoustic alarms are still audible and distracting for both, healthcare professionals and patients. Our goal is to convey alarms directly to the responsible nurse. By augmenting Google Glass with additional LEDs and vibration motors, we aim to alert nurses unobtrusively using peripheral light, vibration and bone-conduction sound. In this work, we describe the development of a prototype for a wearable, multimodal patient monitoring system.

References

  1. Vanessa Cobus and Wilko Heuten. 2019. To Beep or Not to Beep? Evaluating Modalities for Multimodal ICU Alarms. Multimodal Technologies Interact. 3, 1 (2019).Google ScholarGoogle Scholar
  2. Maria M. Cvach, Robert J. Frank, Pete Doyle, and Zeina Khouri Stevens. 2014. Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. Journal of Nursing Care Quality 29, 1 (2014), 9--18.Google ScholarGoogle ScholarCross RefCross Ref
  3. Thomas FE Drake-Brockman, Amitava Datta, and Britta S von Ungern-Sternberg. 2016. Patient monitoring with Google Glass: a pilot study of a novel monitoring technology. Pediatric Anesthesia 26, 5 (2016), 539--546.Google ScholarGoogle ScholarCross RefCross Ref
  4. Lauren Kolodzey, Peter D Grantcharov, Homero Rivas, Marlies P Schijven, and Teodor P Grantcharov. 2017. Wearable technology in the operating room: a systematic review. BMJ Innovations 3, 1 (2017), 55--63.Google ScholarGoogle ScholarCross RefCross Ref
  5. David Liu, Simon A Jenkins, and Penelope M Sanderson. 2009. Patient monitoring with head-mounted displays. Current Opinion in Anesthesiology 22, 6 (2009), 796--803.Google ScholarGoogle ScholarCross RefCross Ref
  6. Michael T Pascale, Penelope Sanderson, David Liu, Ismail Mohamed, Birgit Brecknell, and Robert G Loeb. 2019. The impact of head-worn displays on strategic alarm management and situation awareness. Human factors (2019), 0018720818814969.Google ScholarGoogle Scholar
  7. Keith J. Ruskin and Dirk Hueske-Kraus. 2015-12. Alarm fatigue: impacts on patient safety. Current Opinion in Anaesthesiology 28, 6 (2015-12), 685--690.Google ScholarGoogle ScholarCross RefCross Ref
  8. Paul Schlosser, Tobias Grundgeiger, and Oliver Happel. 2018. Multiple patient monitoring in the operating room using a head-mounted display. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, LBW037. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bradford D. Winters, Maria M. Cvach, Christopher P. Bonafide, Xiao Hu, Avinash Konkani, Michael F. O'Connor, Jeffrey M. Rothschild, Nicholas M. Selby, Michele M. Pelter, Barbara McLean, Sandra L. Kane-Gill, and Society for Critical Care Medicine Alarm and Alert Fatigue Task Force. 2018. Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms With Intent to Reduce Alarm Fatigue. Critical Care Medicine 46, 1 (2018), 130--137.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Integration of multimodal alarms into Google Glass: demo

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        PerDis '19: Proceedings of the 8th ACM International Symposium on Pervasive Displays
        June 2019
        223 pages
        ISBN:9781450367516
        DOI:10.1145/3321335

        Copyright © 2019 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 June 2019

        Check for updates

        Qualifiers

        • demonstration

        Acceptance Rates

        PerDis '19 Paper Acceptance Rate26of67submissions,39%Overall Acceptance Rate213of384submissions,55%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader