A Novel Human Activity Recognition Model for Smartphone Authentication
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10258-x.pdf
Reference38 articles.
1. Erdaş, Ç. B., & Güney, S. (2021). Human activity recognition by using different deep learning approaches for wearable sensors. Neural Processing Letters, 53, 1795–1809.
2. Chen, K., Yao, L., Zhang, D., Wang, X., Chang, X., & Nie, F. (2020). A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1747–1756.
3. Huang, J., Lin, S., Wang, N., Dai, G., Xie, Y., & Zhou, J. (2020). TSE-CNN: A two-stage end-to-end CNN for human activity recognition. IEEE Journal of Biomedical and Health Informatics, 24(1), 292–299.
4. Wang, K., He, J., & Zhang, L. (2019). Attention-based convolutional neural network for weakly labeled human activities’ recognition with wearable sensors. IEEE Sensors Journal, 19(17), 7598–7604.
5. Yan, H., Zhang, Y., Wang, Y., & Xu, K. (2020). WiAct: A passive WiFi-based human activity recognition system. IEEE Sensors Journal, 20(1), 296–305.
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