Human Fall Detection from Acceleration Measurements Using a Recurrent Neural Network
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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.
KeywordsHuman fall detection Recurrent neural network Data augmentation Acceleration
This work was supported by the European Project: ICT4LIFE http://ict4life.eu/ Grant no. 690090 within the H2020 Research and Innovation Programme.
Conflict of Interest
The authors declare that they have no conflict of interest.
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