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Title:

Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks

Document type:
Konferenzbeitrag
Author(s):
Um, Terry T.; Pfister, Franz M. J.; Pichler, Daniel; Endo, Satoshi; Lang, Muriel; Hirche, Sandra; Fietzek, Urban; Kulić, Dana
Abstract:
While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs...     »
Book / Congress title:
Proceedings of the 19th ACM International Conference on Multimodal Interaction - ICMI 2017
Publisher:
ACM Press
Date of publication:
01.01.2017
Year:
2017
Print-ISBN:
9781450355438
Fulltext / DOI:
doi:10.1145/3136755.3136817
CC license:
by, http://creativecommons.org/licenses/by/4.0
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