Critical care environments are inherently complex and dynamic. Assessment of workflow in such environments is not trivial. While existing approaches for workflow analysis such as ethnographic observations and interviewing provide contextualized information about the overall workflow, they are limited in their ability to capture the workflow from all perspectives. This paper presents a tool for automated activity recognition that can provide an additional point of view. Using data captured by Radio Identification (RID) tags and Hidden Markov Models (HMMs), key activities in the environment can be modeled and recognized. The proposed method leverages activity recognition systems to provide a snapshot of workflow in critical care environments. The activities representing the workflow can be extracted and replayed using virtual reality environments for further analysis.
|Original language||English (US)|
|Number of pages||5|
|Journal||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|State||Published - 2009|
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