Abstract
The loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. The popularization of smartwatches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smartwatch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. The results show high accuracy and robust classifiers able to detect falls.
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Notes
- 1.
All datasets are available on http://atc1.aut.uah.es/~david/ideal2016.
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Acknowledgements
The authors thank the contribution of Isabel Pascual Benito, Francisco López Martínez and Helena Hernández Martínez, from Department of Nursing and Physiotherapy of the University of Alcalá, for their help designing and supervising the simulated falls procedure. This work is supported by UAH (2015/00297/001), JCLM (PEII-2014-015-A) and MINCECO (TIN2014-56494-C4-4-P).
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Villaverde, A.C., R-Moreno, M.D., Barrero, D.F., Rodriguez, D. (2016). Triaxial Accelerometer Located on the Wrist for Elderly People’s Fall Detection. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_56
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DOI: https://doi.org/10.1007/978-3-319-46257-8_56
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