Abstract
In this study, the fall detection method is carried out as stated on [1, 11]; a simple finite state machine is used to process acceleration data in sliding windows and whenever a fall-like event is found, features are extracted from this data. Using some clustering and classification algorithms described here, the event is classified as FALL or NOT_FALL. This research evaluates the performance of different proposed clustering and classification methods. It makes use of a new dataset, with data gathered by a wearable device placed on the wrist and used by several members of the research team and an emergency rescue training manikin under different fall scenarios to simulate the falls. A 10-fold cross-validation is also made to evaluate these methods on unseen data.
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This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R.
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Fáñez, M., Villar, J.R., de la Cal, E., González, V.M., Sedano, J. (2020). Feature Clustering to Improve Fall Detection: A Preliminary Study. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_21
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