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 |
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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 | |
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 |
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Abbreviated title | ICMI 2017 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 13/11/17 → 17/11/17 |
Internet address |
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Keywords
- Convolutional neural networks
- Data augmentation
- Health monitoring
- Parkinson's disease
- Wearable sensor