Abstract
Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application.
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Acknowledgements
This research has been funded by Universidad Panamericana through the grant “Fomento a la Investigación UP 2018,” under project code UP-CI-2018-ING-MX-04.
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Ponce, H., Martínez-Villaseñor, L., Núñez-Martínez, J., Moya-Albor, E., Brieva, J. (2020). Open Source Implementation for Fall Classification and Fall Detection Systems. In: Ponce, H., Martínez-Villaseñor, L., Brieva, J., Moya-Albor, E. (eds) Challenges and Trends in Multimodal Fall Detection for Healthcare. Studies in Systems, Decision and Control, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-38748-8_1
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