SONAR, a nursing activity dataset with inertial sensors
Journal
Scientific Data
Date Issued
2023-10-20
Author(s)
Konak, Orhan
Döring, Valentin
Fiedler, Tobias
Liebe, Lucas
Masopust, Leander
Postnov, Kirill
Sauerwald, Franz
Treykorn, Felix
Wischmann, Alexander
Kalabakov, Stefan
Gjoreski, Hristijan
Luštrek, Mitja
Arnrich, Bert
DOI
10.1038/s41597-023-02620-2
Abstract
<jats:title>Abstract</jats:title><jats:p>Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.</jats:p>