Abstract
Fall detection is an important task in telemedicine. In this paper an approach based on supervised knowledge extraction is presented. A fall recordings database is analyzed offline and a set of IF...THEN rules is obtained. This way, also selection of the most relev ant features for fall assessment is automatically carried out. The approach is embedded within a real-time mobile monitoring system, and is used to discriminate in real time normal daily activities from falls. If the data collected in real time by wearable sensors of the system allow recognizing a fall, suitable alarms are automatically generated.
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© 2014 Springer International Publishing Switzerland
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Sannino, G., De Falco, I., De Pietro, G. (2014). Effective Supervised Knowledge Extraction for an mHealth System for Fall Detection. In: Roa Romero, L. (eds) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-00846-2_341
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DOI: https://doi.org/10.1007/978-3-319-00846-2_341
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00845-5
Online ISBN: 978-3-319-00846-2
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