Human Activity Recognition with IMU and Vital Signs Feature Fusion
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-98358-1_23
Reference21 articles.
1. Chen, J., Sun, Y., Sun, S.: Improving human activity recognition performance by data fusion and feature engineering. Sensors 21(3), 692 (2021)
2. Chen, L., Liu, X., Peng, L., Wu, M.: Deep learning based multimodal complex human activity recognition using wearable devices. Appl. Intell. 51(6), 4029–4042 (2020). https://doi.org/10.1007/s10489-020-02005-7
3. Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2016)
4. Doewes, A., Swasono, S.E., Harjito, B.: Feature selection on human activity recognition dataset using minimum redundancy maximum relevance. In: 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 171–172. IEEE (2017)
5. Dua, N., Singh, S.N., Semwal, V.B.: Multi-input CNN-GRU based human activity recognition using wearable sensors. Computing 103(7), 1461–1478 (2021). https://doi.org/10.1007/s00607-021-00928-8
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