Semi-Supervised Learning for Time Series Collected at a Low Sampling Rate
Author:
Affiliation:
1. KAIST, Daejeon, Republic of Korea
2. Samsung Electronics Co., Ltd., Suwon-si, Republic of Korea
Funder
Mobile eXperience Business, Samsung Electronics Co., Ltd.
National Research Foundation of Korea
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3672033
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2. Davide Anguita et al. 2013. A public domain dataset for human activity recognition using smartphones.. In ESANN. 437--442.
3. Shaojie Bai et al. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).
4. Oresti Banos et al. 2014. mHealthDroid: A novel framework for agile development of mobile health applications. In IWAAL. 91--98.
5. Effects of ECG sampling rate on QT interval variability measurement
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