Abstract
In this paper we present the results of an experiment with 16 subjects performing activities of daily living and simulated falls. We used a triaxial accelerometer to track the subjects’ movements. From the accelerometer data we calculated five different features that are used for fall detection. Contingency tables were built based on the collected dataset and ROC curves were plotted. Optimal thresholds for every feature and corresponding sensitivities and specificities were calculated based on the ROC curve analysis.
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Šeketa, G., Vugrin, J., Lacković, I. (2018). Optimal Threshold Selection for Acceleration-Based Fall Detection. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-10-7419-6_26
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DOI: https://doi.org/10.1007/978-981-10-7419-6_26
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