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arXiv 1706.00527cs.CV
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使用卷积神经网络对用于帕金森病监测的可穿戴传感器数据进行数据增强

Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

University of Waterloo（滑铁卢大学）；Ludwig-Maximilians-Univ. München（慕尼黑大学）；Technical University of Munich（慕尼黑工业大学）；Schön Klinik München Schwabing（慕尼黑施瓦宾 Schön 医院）

Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, Dana Kulić

备注：
ICMI2017 (oral session)
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摘要：

虽然卷积神经网络（CNN）已成功应用于许多具有挑战性的分类应用，但它们通常需要大型数据集进行训练。当标记数据的可用性有限时，数据增强是CNN的关键预处理步骤。然而，可穿戴传感器数据的数据增强尚未得到深入研究。在本文中，提出了各种数据增强方法的可穿戴传感器数据。所提出的方法和CNN被应用于帕金森病患者的运动状态的分类，这是具有挑战性的，由于小的数据集大小，嘈杂的标签，和大的类内变异性。适当的增强使分类性能从77.54%提高到86.88%。

英文摘要：

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\%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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