MCTN: A Multi-Channel Temporal Network for Wearable Fall Prediction
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-43427-3_24
Reference17 articles.
1. Blunda, L.L., Gutiérrez-Madroñal, L., Wagner, M.F., Medina-Bulo, I.: A wearable fall detection system based on body area networks. IEEE Access 8, 193060–193074 (2020). https://doi.org/10.1109/ACCESS.2020.3032497
2. Challa, S.K., Kumar, A., Semwal, V.B.: A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. Visual Comput. 38, 1–15 (2021). https://doi.org/10.1007/s00371-021-02283-3
3. Dirgová Luptáková, I., Kubovčík, M., Pospíchal, J.: Wearable sensor-based human activity recognition with transformer model. Sensors 22(5), 1911 (2022). https://doi.org/10.3390/s22051911
4. Hemmatpour, M., Ferrero, R., Gandino, F., Montrucchio, B., Rebaudengo, M.: Internet of Things for fall prediction and prevention. J. Comput. Methods Sci. Eng. 18(2), 511–518 (2018). https://doi.org/10.3233/JCM-180806
5. Howcroft, J., Kofman, J., Lemaire, E.D.: Prospective fall-risk prediction models for older adults based on wearable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1812–1820 (2017). https://doi.org/10.1109/TNSRE.2017.2687100
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