Machine Learning Approaches for Human Activity Recognition Based on Multimodal Body Sensors
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-71187-0_90
Reference12 articles.
1. Nweke, H.F., Teh, Y.W., Mujtaba, G., Alo, U.R., Al-garadi, M.A.: Multi-sensor fusion based on multiple classifier systems for human activity identification. Hum.-Centric Comput. Inf. Sci. 9, 34 (2019)
2. Oguntala, G.A., et al.: SmartWall: novel RFID-enabled ambient human activity recognition using machine learning for unobtrusive health monitoring. IEEE Access 7, 68022–68033 (2019). https://doi.org/10.1109/ACCESS.2019.2917125
3. De la Hoz, E., Ariza, P., Medina, J., Espinilla, M.: Sensor-based datasets for human activity recognition – a systematic review of literature. IEEE Access 1 (2018). https://doi.org/10.1109/ACCESS.2018.2873502
4. Abidine, M., Fergani, L., Fergani, B., Fleury, A.: Improving human activity recognition in smart homes. Int. J. E-Health Med. Commun. (IJEHMC) 6, 19–37 (2015). https://doi.org/10.4018/IJEHMC.2015070102
5. Verma, A., Mehta, S.: A comparative study of ensemble learning methods for classification in bioinformatics. In: 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, Noida, pp. 155–158 (2017). https://doi.org/10.1109/CONFLUENCE.2017.7943141
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