Abstract
Fall avoidance systems reduce injuries due to unintentional falls, but most of them are fall detections that activate an alarm after the fall occurrence. Since predicting a fall is the most promising approach to avoid a fall injury, this study proposes a method based on new features and multilayer perception that outperforms state-of-the-art approaches. Since accelerometer and gyroscope embedded in a smartphone are recognized to be precise enough to be used in fall avoidance systems, they have been exploited in an experimental analysis in order to compare the proposal with state-of-the-art approaches. The results have shown that the proposed approach improves the accuracy from 83% to 90%.
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Acknowledgement
This work was partially supported by the grant “Bando Smart Cities and Communities”, OPLON Project (OPportunities for active and healthy LONgevity) funded by the Italian Ministry for University.
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Hemmatpour, M., Ferrero, R., Montrucchio, B., Rebaudengo, M. (2017). A Neural Network Model Based on Co-occurrence Matrix for Fall Prediction. In: Perego, P., Andreoni, G., Rizzo, G. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-319-58877-3_32
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DOI: https://doi.org/10.1007/978-3-319-58877-3_32
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