Abstract
In this paper we discuss methods to classify behavioural differences of individuals before and after a hospital stay, which can be detected using only data recorded with a sensor floor. The used sensor floor offers the possibility of recording the movement behaviour of individuals as inconspicuously as possible because it is laid under the normal floor covering. The aspect of unobtrusive monitoring promises a versatile use, especially in nursing, which benefits both medical staff and patients. A Multi-Layer Perceptron (MLP), a Support Vector Machine (SVM), a Gaussian Naive Bayes (GNB) and a Random Decision Forest (RDF) were used to classify the data. The results of the methods are represented with the metrics Accuracy, F1-Score, Precision, Recall/Sensitivity and Specificity. For MLP, SVM and RDF the results are very good and show that behavioural changes can be detected using only data recorded by a sensor floor.
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Acknowledgement
This project was funded in parts by the Christl-Lauterbach-Stiftung. We thank Anika Fischer and Markus Eiba for their work in the data collection process at the senior residence.
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Liebenow, L., Walter, J., Hoffmann, R., Steinhage, A., Grzegorzek, M. (2022). Classifying Changes in Motion Behaviour Due to a Hospital Stay Using Floor Sensor Data – A Single Case Study. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_1
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