Biometric Gait Analysis Using Wrist-Mounted Wearable Sensors
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42823-4_3
Reference11 articles.
1. Um, T.T., Pfister, F.M., Pichler, D., et al.: Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK, 13–17 (2017)
2. Eyobu, O.S., Han, D.: Feature representation and data augmentation for human classification based on wearable IMU sensor data using a deep LSTM neural network. Sensors 18, 2892 (2018)
3. Sawicki, A.: Augmentation of accelerometer and gyroscope signals in biometric gait systems. In: Saeed, K., Dvorský, J. (eds.) Computer Information Systems and Industrial Management, CISIM 2022. LNCS, vol. 13293, pp. 32–45. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10539-5_3
4. Zou, Q., Wang, Y., Wang, Q., Zhao, Y., et al.: Deep learning-based gait recognition using smartphones in the wild, arXiv:1811.0033 (2018)
5. Gadaleta, M., Merelli, L., Rossi, M.: Human authentication from ankle motion data using convolutional neural networks. In: 2016 IEEE Statistical Signal Processing Workshop (SSP), Palma de Mallorca, Spain, pp. 1–5 (2016). https://doi.org/10.1109/SSP.2016.7551815
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