Human fall detection from acceleration measurements using a Recurrent Neural Network

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T. Theodoridis, V. Solachidis, N. Vretos and P. Daras, “Human fall detection from acceleration measurements using a Recurrent Neural Network”, Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece, 2017.

Abstract

In this work, a method for human fall detection is presented based on Recurrent Neural Networks. The ability of these networks to process and encode sequential data, such as acceleration measurements from body-worn sensors, makes them ideal candidates for this task. Furthermore, since such networks can benefit greatly from additional data during training, the use of a data augmentation procedure involving random 3D rotations has been investigated. When evaluated on the publicly available URFD dataset, the proposed method achieved better results compared to other methods.

 

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