Publisher
Springer Nature Switzerland
Reference17 articles.
1. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3–11 (2019). https://doi.org/10.1016/j.patrec.2018.02.010
2. Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y.: Physical Human Activity Recognition Using Wearable Sensors. Sensors 15, 31314–31338 (2015). https://doi.org/10.3390/s151229858
3. Anguita, D., et al.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, pp. 437–442 (2013)
4. Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., Luaces, M.R.: A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors 20, 2200 (2020). https://doi.org/10.3390/s20082200
5. Daghistani, T., Alshammari, R.: Improving accelerometer-based activity recognition by using ensemble of classifiers. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 7(5), 128–133 (2016). https://doi.org/10.14569/IJACSA.2016.070520