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

doi:10.1145/3136755.3136817

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 proceeding › Conference Paper › Research › peer-review

154 Citations (Scopus)

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 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 language	English
Title of host publication	Proceedings of the 19th ACM International Conference on Multimodal Interaction
Editors	Edward Lank, Alessandro Vinciarelli, Eve Hoggan, Sriram Subramanian, Stephen A. Brewster
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	216-220
Number of pages	5
ISBN (Electronic)	9781450355438
DOIs	https://doi.org/10.1145/3136755.3136817
Publication status	Published - 3 Nov 2017
Externally published	Yes
Event	International Conference on Multimodal Interfaces 2017 - Glasgow, United Kingdom Duration: 13 Nov 2017 → 17 Nov 2017 Conference number: 19th https://icmi.acm.org/2017/ (conference website) https://dl.acm.org/doi/proceedings/10.1145/3136755 (Proceedings)

Conference

Conference	International Conference on Multimodal Interfaces 2017
Abbreviated title	ICMI 2017
Country	United Kingdom
City	Glasgow
Period	13/11/17 → 17/11/17
Internet address	https://icmi.acm.org/2017/ (conference website) https://dl.acm.org/doi/proceedings/10.1145/3136755 (Proceedings)

Keywords

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

Access to Document

10.1145/3136755.3136817

Link to publication in Scopus

Cite this

Um, T. T., Pfister, F. M. J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., & Kulic, D. (2017). Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks. In E. Lank, A. Vinciarelli, E. Hoggan, S. Subramanian, & S. A. Brewster (Eds.), Proceedings of the 19th ACM International Conference on Multimodal Interaction (pp. 216-220). Association for Computing Machinery (ACM). https://doi.org/10.1145/3136755.3136817

Um, Terry T. ; Pfister, Franz M.J. ; Pichler, Daniel ; Endo, Satoshi ; Lang, Muriel ; Hirche, Sandra ; Fietzek, Urban ; Kulic, Dana. / Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction. editor / Edward Lank ; Alessandro Vinciarelli ; Eve Hoggan ; Sriram Subramanian ; Stephen A. Brewster. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 216-220

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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 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%.",

keywords = "Convolutional neural networks, Data augmentation, Health monitoring, Parkinson's disease, Wearable sensor",

author = "Um, {Terry T.} and Pfister, {Franz M.J.} and Daniel Pichler and Satoshi Endo and Muriel Lang and Sandra Hirche and Urban Fietzek and Dana Kulic",

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Um, TT, Pfister, FMJ, Pichler, D, Endo, S, Lang, M, Hirche, S, Fietzek, U & Kulic, D 2017, Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks. in E Lank, A Vinciarelli, E Hoggan, S Subramanian & SA Brewster (eds), Proceedings of the 19th ACM International Conference on Multimodal Interaction. Association for Computing Machinery (ACM), New York NY USA, pp. 216-220, International Conference on Multimodal Interfaces 2017, Glasgow, United Kingdom, 13/11/17. https://doi.org/10.1145/3136755.3136817

Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks. / Um, Terry T.; Pfister, Franz M.J.; Pichler, Daniel; Endo, Satoshi; Lang, Muriel; Hirche, Sandra; Fietzek, Urban; Kulic, Dana.

Proceedings of the 19th ACM International Conference on Multimodal Interaction. ed. / Edward Lank; Alessandro Vinciarelli; Eve Hoggan; Sriram Subramanian; Stephen A. Brewster. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 216-220.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Um TT, Pfister FMJ, Pichler D, Endo S, Lang M, Hirche S et al. Data augmentation of wearable sensor data for Parkinson's disease monitoring using convolutional neural networks. In Lank E, Vinciarelli A, Hoggan E, Subramanian S, Brewster SA, editors, Proceedings of the 19th ACM International Conference on Multimodal Interaction. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 216-220 https://doi.org/10.1145/3136755.3136817