Comparison of automated activity recognition to provider observations of patient mobility in the ICU

Nishi Rawat, Vishal Rao, Michael Peven, Christine Shrock, Austin Reiter, Suchi Saria, Haider Ali

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To compare noninvasive mobility sensor patient motion signature to direct observations by physicians and nurses. Design: Prospective, observational study. Setting: Academic hospital surgical ICU. Patients and Measurements: A total of 2,426 1-minute clips from six ICU patients (development dataset) and 4,824 1-minute clips from five patients (test dataset). Interventions: None. Main Results: Noninvasive mobility sensor achieved a minute-level accuracy of 94.2% (2,138/2,272) and an hour-level accuracy of 81.4% (70/86). Conclusions: The automated noninvasive mobility sensor system represents a significant departure from current manual measurement and reporting used in clinical care, lowering the burden of measurement and documentation on caregivers.

Original languageEnglish (US)
Pages (from-to)1232-1234
Number of pages3
JournalCritical care medicine
Volume47
Issue number9
DOIs
StatePublished - 2019

Keywords

  • Artificial intelligence
  • Computer vision
  • Intensive care unit
  • Mobility
  • Rehabilitation

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

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