A Novel CNN-SVM Hybrid Model for Human Activity Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60629-8_27
Reference15 articles.
1. Dang, L.M., Min, K., Wang, H., Piran, M.J., Lee, C.H., Moon, H.: Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn. 108, 107561 (2020)
2. Serpush, F., Menhaj, M.B., Masoumi, B., Karasfi, B.: Wearable sensor-based human activity recognition in the smart healthcare system. Comput. Intell. Neurosci. 2022, 1391906 (2022). https://doi.org/10.1155/2022/1391906
3. Gul, M.A., Yousaf, M.H., Nawaz, S., Rehman, Z.U., Kim, H.: Patient monitoring by abnormal human activity recognition based on CNN architecture. Electronics 9(12), 1993 (2020). https://doi.org/10.3390/electronics9121993
4. Kozina, S., Gjoreski, H., Gams, M., Luštrek, M.: Efficient activity recognition and fall detection using accelerometers. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds.) Evaluating AAL Systems Through Competitive Benchmarking. CCIS, vol. 386, pp. 13–23. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41043-7_2
5. Shahar, N., Ghazali, N., Asâari, M., Swee, T.: Wearable inertial sensor for human activity recognition in field hockey: influence of sensor combination and sensor location. J. Phys. Conf. Ser. 1529(2), 022015 (2020)
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