Recognizing Hospital Care Activities with a Pocket Worn Smartphone – Best Paper Award

Our paper “Recognizing Hospital Care Activities with a Pocket Worn Smartphone” received the Best Paper Award at the 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014).

This has been a joint effort of the German Research Centre for Artificial Intelligence and the University of Trento, and concerns the problem of “semantic gap” between machine and human semantics.

Abstract: 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.

Index Terms: Activity Recognition, health care documentation, real-world study

Citation: Gernot Bahle, Agnes Gruenerbl, Enrico Bignotti, Mattia Zeni, Fausto Giunchiglia and Paul Lukowicz (2014): “Recognizing Hospital Care Activities with a Pocket Worn Smartphone”, 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014, http://mobicase.org/2014/show/home)

You can find the complete list of proceedings here: http://proceedings.dev.icstweb.eu/2014/mobicase2014/file-storage/index.html#submissions

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About P. Andreadis

Pre-Doctoral Research Assistant in AI and Social Computation @ University of Edinburgh.

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