Comparison of Automated Activity Recognition to Provider Observations of Patient Mobility in the ICU*. Issue 9 (September 2019)

• Record Type: Journal Article
• Authors: Rawat, Nishi; Rao, Vishal; Peven, Michael; Shrock, Christine; Reiter, Austin; Saria, Suchi; Ali, Haider
• Is Part Of: Critical care medicine. Volume 47:Issue 9(2019)
• Publication Date: 2019-09
• Subjects: artificial intelligence -- computer vision -- intensive care unit -- mobility -- rehabilitation; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000003852
• Languages: English
• ISSNs: 0090-3493

Abstract

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. Abstract : Supplemental Digital Content is available in the text.