Abstract
Fall detection represents an important issue when dealing with Ambient Assisted Living for the elder. The vast majority of fall detection approaches have been developed for healthy and relatively young people. Moreover, plenty of these approaches make use of sensors placed on the hip. Considering the focused population of elderly people, there are clear differences and constraints. On the one hand, the patterns and times in the normal activities -and also the falls- are different from younger people: elders move slowly. On the second hand, solutions using uncomfortable sensory systems would be rejected by many candidates. In this research, one of the proposed solutions in the literature has been adapted to use a smartwatch on a wrist, solving some problems and modifying part of the algorithm. The experimentation includes a publicly available dataset. Results point to several enhancements in order to be adapted to the focused population.
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Acknowledgments
This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2014-56967-R and MINECO-TIN2017-84804-R, and FC-15-GRUPIN14-073 (Regional Ministry of Principality of Asturias).
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Barri Khojasteh, S., Villar, J.R., de la Cal, E., González, V.M., Sedano, J., Yazg̈an, H.R. (2018). Evaluation of a Wrist-Based Wearable Fall Detection Method. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_31
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