Intelligent Emergency Department: Validation of Sociometers to Study Workload

Denny Yu, Renaldo C. Blocker, Mustafa Y. Sir, M. Susan Hallbeck, Thomas R. Hellmich, Tara Cohen, David M. Nestler, Kalyan S. Pasupathy

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Sociometers are wearable sensors that continuously measure body movements, interactions, and speech. The purpose of this study is to test sociometers in a smart environment in a live clinical setting, to assess their reliability in capturing and quantifying data. The long-term goal of this work is to create an intelligent emergency department that captures real-time human interactions using sociometers to sense current system dynamics, predict future state, and continuously learn to enable the highest levels of emergency care delivery. Ten actors wore the devices during five simulated scenarios in the emergency care wards at a large non-profit medical institution. For each scenario, actors recited prewritten or structured dialogue while independent variables, e.g., distance, angle, obstructions, speech behavior, were independently controlled. Data streams from the sociometers were compared to gold standard video and audio data captured by two ward and hallway cameras. Sociometers distinguished body movement differences in mean angular velocity between individuals sitting, standing, walking intermittently, and walking continuously. Face-to-face (F2F) interactions were not detected when individuals were offset by 30°, 60°, and 180° angles. Under ideal F2F conditions, interactions were detected 50 % of the time (4/8 actor pairs). Proximity between individuals was detected for 13/15 actor pairs. Devices underestimated the mean duration of speech by 30–44 s, but were effective at distinguishing the dominant speaker. The results inform engineers to refine sociometers and provide health system researchers a tool for quantifying the dynamics and behaviors in complex and unpredictable healthcare environments such as emergency care.

Original languageEnglish (US)
Article number53
Pages (from-to)1-12
Number of pages12
JournalJournal of Medical Systems
Issue number3
StatePublished - Mar 1 2016


  • Clinical engineering learning lab
  • Emergency department
  • Information and communication technology (ICT)
  • Intelligent healthcare
  • Sensor technology

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management


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