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?:abstract
  • A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to spend more than one hour per day for documentation efforts.In this paper a novel concept for reducing the manual documentation effort by leveraging methods of human activity recognition is introduced and a corresponding dataset is published. The dataset captures different care activities like repositioning, sitting up, transfer and patient mobilization using body worn sensors in a realistic setting with multiple patients and caregivers.For evaluation of the data, two experimental setups are presented: an unsegmented case, where the duration of the care activity is unknown and a segmented case, where the beginning and the end of the activity is known beforehand. First experiments show the feasibility of recognizing care activities using different types of Neural Networks. (xsd:string)
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  • (ZIS) (xsd:string)
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  • GESIS-Literaturpool (xsd:string)
?:dateModified
  • 2023 (xsd:gyear)
?:datePublished
  • 2023 (xsd:gyear)
?:doi
  • 10.1145/3558884.3558891 ()
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  • 1 (xsd:string)
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  • english (xsd:string)
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  • 9781450396240 ()
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?:name
  • Dataset and Methods for Recognizing Care Activities (xsd:string)
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  • inproceedings (xsd:string)
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?:sourceCollection
  • Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (xsd:string)
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  • Bibsonomy (xsd:string)
  • In Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence, edited by Aehnelt, Mario and Kirste, Thomas, 1-8, Association for Computing Machinery, 2023 (xsd:string)
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  • 19.09.-20.09.2022 (xsd:gyear)
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  • 2023 (xsd:string)
  • EX (xsd:string)
  • ZIS (xsd:string)
  • ZIS_input2023 (xsd:string)
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  • english (xsd:string)
  • inproceedings (xsd:string)
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  • 8 (xsd:string)
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