Recognizing Hospital Care Activities with a Coat Pocket Worn Smartphone

Gernot Bahle, Agnes Grünerbl, Enrico Bignotti, Mattia Zeni, Fausto Giunchiglia, Paul Lukowicz

In: Proceedings of the International Conference on Mobile Computing, Applications and Services. International Conference on Mobile Computing, Applications and Services (MobiCase-2014) November 6-7 Austin Texas United States IEEE - Xplore 2014.


In this work, we show how a smart-phone worn unobtrusively in a nurses coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor node worn in such an (from the sensing point of view) unfavorable location can still recognize complex, sometimes subtle activities. We evaluate our approach in a large real life dataset from day to day hospital operation. In total, 4 runs of patient care per day were collected for 14 days at a geriatric ward and annotated in high detail by following the performing nurses for the entire duration. This amounts to over 800 hours of sensor data including acceleration, gyroscope, compass, wifi and sound annotated with groundtruth at less than 1min resolution.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz