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Poster abstract: Maximizing accuracy of fall detection and alert systems based on 3D convolutional neural network

Title
Poster abstract: Maximizing accuracy of fall detection and alert systems based on 3D convolutional neural network
Authors
Hwang, SeokhyunAhn, DaeHanPark, HominPark, Taejoon
DGIST Authors
Hwang, Seokhyun; Ahn, DaeHan; Park, Homin; Park, Taejoon
Issue Date
2017
Citation
2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017, 343-344
Type
Conference
Article Type
Conference Paper
ISBN
9781450000000
Abstract
We present a deep-learning-based approach to maximize the accuracy and reliability of vision-based fall detection and alert systems. The proposed approach utilizes a 3D convolutional neural network (3D-CNN) to analyze the continuous motion data obtained from depth cameras and exploits a data augmentation method to do away with overfitting. Our preliminary evaluation results demonstrate that it achieves the classification accuracy of up to 96.9%. © 2017 ACM.
URI
http://hdl.handle.net/20.500.11750/4318
DOI
10.1145/3054977.3057314
Publisher
Association for Computing Machinery, Inc
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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