High accuracy human activity recognition using machine learning and wearable devices’ raw signals
Author:
Affiliation:
1. School of Engineering, Department of Civil Engineering, Lab of Mathematics and Informatics, Democritus University of Thrace, Xanthi, Greece
Publisher
Informa UK Limited
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)
Link
https://www.tandfonline.com/doi/pdf/10.1080/24751839.2021.1987706
Reference42 articles.
1. Boser, B. E., Guyon, I. M. & Vapnik, V. N. (1992, July 27–29). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on computational learning theory (pp. 144–152). https://doi.org/10.1145/130385.130401
2. ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors
3. Breiman, L. (1994). Bagging predictors (Technical Report 421). Department of Statistics, University of California at Berkeley.
4. Breiman, L. (1997). Arcing the edge (Technical Report 486). Statistics Department, University of California at Berkeley.
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