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Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Neural Network Model Based on Co-occurrence Matrix for Fall Prediction
verfasst von : Masoud Hemmatpour, Renato Ferrero, Bartolomeo Montrucchio, Maurizio Rebaudengo
Erschienen in: Wireless Mobile Communication and Healthcare
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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%.