Abstract
This study focuses on the performance of a fall detection method using data coming from real falls performed by relatively young people and the application of this technique in the case of an elder person. Although the vast majority of studies concerning fall detection place the sensory on the waist, in this research the wearable device must be placed on the wrist because it’s usability. A first pre-processing stage is carried out as stated in [1, 17]; this stage detects the most relevant points to label. This study analyzes the suitability of different models in solving this classification problem: a feed-forward Neural Network and a rule based system generated with the C5.0 algorithm. A discussion about the results and the deployment issues is included.
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Acknowledgment
This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2014-56967-R and MINECO-TIN2017-84804-R.
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Khojasteh, S.B., Villar, J.R., de la Cal, E., González, V.M., Sedano, J. (2019). Fall Detection Analysis Using a Real Fall Dataset. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_32
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