Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation (Preprint)
AuthorMarini, G; Tag, B; Goncalves, J; Velloso, E; Jurdak, R; Capurro, D; McCarthy, C; Shearer, W; Kostakos, V
Source TitleJMIR mHealth and uHealth
University of Melbourne Author/sMarini, Gabriele; Velloso, Eduardo; Kostakos, Vassilis; Goncalves, Jorge; Tag, Benjamin; Capurro, Daniel
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsMarini, G., Tag, B., Goncalves, J., Velloso, E., Jurdak, R., Capurro, D., McCarthy, C., Shearer, W. & Kostakos, V. (2020). Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation (Preprint). JMIR mHealth and uHealth, https://doi.org/10.2196/preprints.19874.
Access StatusOpen Access
Open Access URLSubmitted version
Submitted version has the title: A System to Quantify Mobility and Occupancy Levels in Clinical Settings: Development and Implementation
Background: The use of location-based data in clinical settings is often limited to real-time monitoring. Here we develop a proximity-based localisation system, and show how its longitudinal analysis can provide operational insights relating to mobility and occupancy in clinical settings. Objective: We measure the accuracy of the system, and algorithmically calculate measures of mobility and occupancy. Methods: We developed a Bluetooth Low Energy proximity-based localisation system, and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. We additionally collected ground-truth data, and used it to validate system performance and accuracy. We conducted a number of analyses to estimate how people move in the hospital, and where they spend their time. Results: Using ground truth data, we estimated our system’s accuracy to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms of the hospital, by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering we showed that the system can differentiate between staff and patients without training. Conclusions: Analysis of longitudinal location-based data can offer rich operational insights to hospitals. Pri- marily, they allow for quick and consistent assessment of new strategies and protocols, and provide a quantitative way to measure their effectiveness.
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