Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks

Terry T. Um, Franz M.J. Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

250 Citations (Scopus)


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 are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.

Original languageEnglish
Title of host publicationProceedings of the 19th ACM International Conference on Multimodal Interaction
EditorsEdward Lank, Alessandro Vinciarelli, Eve Hoggan, Sriram Subramanian, Stephen A. Brewster
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450355438
Publication statusPublished - 3 Nov 2017
Externally publishedYes
EventInternational Conference on Multimodal Interfaces 2017 - Glasgow, United Kingdom
Duration: 13 Nov 201717 Nov 2017
Conference number: 19th (conference website) (Proceedings)


ConferenceInternational Conference on Multimodal Interfaces 2017
Abbreviated titleICMI 2017
Country/TerritoryUnited Kingdom
Internet address


  • Convolutional neural networks
  • Data augmentation
  • Health monitoring
  • Parkinson's disease
  • Wearable sensor

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