Using Synthetic Data to Improve the Accuracy of Human Activity Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48642-5_16
Reference16 articles.
1. Pires, I.M., Hussain, F., Marques, G., Garcia, N.M.: Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques. Comput. Biol. Med. 135, 104638 (2021)
2. Murtaza, H., Ahmed, M., Khan, N.F., Murtaza, G., Zafar, S., Bano, A.: Synthetic data generation: state of the art in health care domain. Comput. Sci. Rev. 48, 100546 (2023)
3. DeOliveira, J., Gerych, W., Koshkarova, A., Rundensteiner, E., Agu, E.: HAR-CTGAN: a mobile sensor data generation tool for human activity recognition. In: IEEE International Conference on Big Data (Big Data), pp. 5233–5242 (2022)
4. Dahmen, J., Cook, D.: SynSys: a synthetic data generation system for healthcare applications. Sensors (Basel), 19(5). 1181 (2019)
5. Rajendran, M., Tan, C.T., Atmosukarto, I., Ng, A.B., See, S.: SynDa: a novel synthetic data generation pipeline for activity recognition. In: IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 373–377 (2022)
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