Abstract
We develop a novel deep learning technique for human fall detection using WiFi Channel State Information (CSI) of a WiFi transmitter and receiver. Different motions in the environment generate distinct features in CSI, which can be fed to a supervised learning machine learning algorithm for training. However, the CSI varies from one environment to another, requiring the collection of environment-specific training data. To overcome this challenge, we propose 1-d convolutional neural network using domain adaptation technique. By adapting to un-labeled data from a new environment, we significantly improve precision and recall, making activity recognition accurate in new environments.
The funding for this research has been provided by Furukawa Electric Group.
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References
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189 (2015)
Shimodaira, H.: Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plan. Inference 90(2), 224–227 (2000)
Shu, R., Bui, H., Narui, H., Ermon, S.: A DIRT-t approach to unsupervised domain adaptation. In: International Conference on Learning Representations (2018)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Yuxi, W., Kaishun, W., Lionel, M.N.: WiFall: device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 16(2), 581–594 (2017)
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. CoRR. arXiv:1611.03530 (2016)
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Narui, H., Shu, R., Gonzalez-Navarro, F.F., Ermon, S. (2020). Domain Adaptation for Human Fall Detection Using WiFi Channel State Information. In: Shaban-Nejad, A., Michalowski, M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-030-24409-5_17
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DOI: https://doi.org/10.1007/978-3-030-24409-5_17
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