Integrated Data Augmentation for Accelerometer Time Series in Behavior Recognition: Roles of Sampling, Balancing, and Fourier Surrogates-Reference-Cited by-同舟云学术

Integrated Data Augmentation for Accelerometer Time Series in Behavior Recognition: Roles of Sampling, Balancing, and Fourier Surrogates

Published:2022-12-15 Issue:24 Volume:22 Page:24230-24241
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Chao¹^ORCID,Minati Ludovico¹^ORCID,Tokgoz Korkut Kaan²^ORCID,Fukawa Masamoto¹,Bartels Jim¹^ORCID,Sihan A¹^ORCID,Takeda Ken-Ichi³,Ito Hiroyuki¹^ORCID

Affiliation:

1. Nano Sensing Unit, Tokyo Institute of Technology, Yokohama, Japan

2. Tokyo Institute of Technology, Research Institute for the Earth Inclusive Sensing (EISESiV), Tokyo, Japan

3. Faculty of Agriculture, Shinshu University, Nagano, Japan

Funder

Center of Innovation Program from Japan Science and Technology Agency and Research Program on Development of Innovative Technology

Bio-oriented Technology Research Advancement Institution, National Agriculture and Food Research Organization

Japan Science and Technology Agency-Support for Pioneering Research Initiated by the Next Generation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9984967/09944648.pdf?arnumber=9944648

Reference55 articles.

1. Surrogate Rehabilitative Time Series Data for Image-based Deep Learning

2. The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

3. Surrogate time series

4. A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification

5. Resampling Strategies for Imbalanced Time Series

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