Recognizing Nursing Activities with AI
Author Information
Author(s): Rahman Arafat, Nahid Nazmun, Schuller Björn, Ahad Md Atiqur Rahman
Primary Institution: Kyushu Institute of Technology, Kitakyushu, Japan
Hypothesis
Can a stacked CNN and random forest ensemble accurately recognize complex nursing activities and identify nurses?
Conclusion
The proposed algorithm achieved high accuracy in recognizing nursing activities and identifying users simultaneously.
Supporting Evidence
- The algorithm achieved 70.6% accuracy for activity recognition on the CARECOM dataset.
- The highest accuracy for user identification was 92.7% on the Heiseikai dataset.
- The study introduced a novel feature called mean min max sum to improve recognition accuracy.
- The ensemble model outperformed traditional CNNs in recognizing complex nursing activities.
Takeaway
This study created a smart system that can tell what nurses are doing and who they are, using data from their smartphones.
Methodology
The study used a two-step feature extraction method and an ensemble of a random forest classifier and a stacked CNN model to analyze data from two benchmark nurse care activity datasets.
Potential Biases
Potential biases may arise from the limited number of participants and the specific contexts in which data were collected.
Limitations
The study faced challenges with class imbalance and the complexity of nursing activities, which may affect the generalizability of the results.
Participant Demographics
All participants were Japanese nurses.
Digital Object Identifier (DOI)
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