Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49018-7_28
Reference25 articles.
1. Banos, O., et al.: Window size impact in human activity recognition. Sensors 14(4), 6474–6499 (2014). https://doi.org/10.3390/s140406474
2. Chen, H., et al.: Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1160–1165. IEEE (2017). https://doi.org/10.1109/BIBM.2017.8217821
3. Chen, W., et al.: Sensecollect: we need efficient ways to collect on-body sensor-based human activity data! Proc. ACM Interact. Mobile Wearable Ubiquitous Technol. 5(3), 1–27 (2021). https://doi.org/10.1145/3478119
4. Coviello, G., Avitabile, G.: Multiple synchronized inertial measurement unit sensor boards platform for activity monitoring. IEEE Sens. J. 20(15), 8771–8777 (2020). https://doi.org/10.1109/JSEN.2020.2982744
5. De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition-a systematic review of literature. IEEE Access 6, 59192–59210 (2018). https://doi.org/10.1109/ACCESS.2018.2873502
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