Open Access
Issue
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
Article Number 07019
Number of page(s) 5
Section Intelligence Algorithms and Application
DOI https://doi.org/10.1051/matecconf/202133607019
Published online 15 February 2021
  1. X. Miao. Fall detection and protection system based on MEMS inertial sensor[D]. Nanchang Hangkong University, (2017). [Google Scholar]
  2. P. Pierleoni, A. Belli, L. Palma, et al. A High Reliability Wearable Device for Elderly Fall Detection[J]. IEEE Sensors Journal, 15(8):4544-4553, (2015). [Google Scholar]
  3. W.Y. Tao. Research on human fall behavior detection based on wearable sensing[D]. University of Electronic Science and Technology of China, (2020). [Google Scholar]
  4. Q.L. Long. Research on human behavior recognition based on improved CNN-LSTM[D]. University of Electronic Science and Technology of China, (2020). [Google Scholar]
  5. J. He, M.W. Zhou, X.Y. Wang. Research on wearable fall detection technology based on Kalman filter and k-NN algorithm[J].Journal of Electronics & Information Technology, 39(11):2627-2634, (2017). [Google Scholar]
  6. S. Angela, López José, Vargas-Bonilla Jesús. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer[J]. Sensors, 18(4):1101, (2018). [Google Scholar]
  7. I.P.E.S. Putra, J. Brusey, E. Gaura, et al. An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection[J]. Sensors, 18(2):20, (2018). [Google Scholar]
  8. N. Pannurat, S. Thiemjarus, E. Nantajeewarawat. A Hybrid Temporal Reasoning Framework for Fall Monitoring[J]. IEEE Sensors Journal, 2017(6):1-1, (2017). [Google Scholar]
  9. Z.N. Li, C.H. Zang, G. Yang, R. Xiang. Design of a fall detection system based on semi-supervised learning[J]. Sensors and Microsystems, 35(10):67-69, (2016). [Google Scholar]
  10. L.T. Xia, L. Zhang. A networked walking stick system for blind people based on K nearest neighbors and dynamic time warping algorithm[J]. Computer Applications, 40(08):2441-2448, (2020). [Google Scholar]
  11. Y.J. Xu, M. Wu. An abnormal event recognition method for gateway metering devices based on multi-feature extraction and deep learning[J/OL]. China Test: 1-8, (2020). [Google Scholar]
  12. G.Y. Xu, J. Zhu, C.Y. Si, et al. Combined hydrological time series prediction model based on CNN and MC[J]. Computer and Modernization, 2019(11):23-28+33, (2019). [Google Scholar]
  13. Y.X. Liu, Y.X. Liu, D.X. Xin, et al. sEMG Motion Intention Recognition Based on Wavelet Time-Frequency Spectrum and ConvLSTM. 1631(1):012150-, (2020). [Google Scholar]
  14. M.J. Xu. DTW-KNN stock trend prediction and stock selection strategy based on the relationship between volume and price[D]. Shandong University, (2019). [Google Scholar]
  15. Y.Q. Chen, X.L. Zhang. Polarimetric SAR image classification based on superpixels and full convolutional networks[J]. Radio Engineering, 50(12):1024-1029, (2020). [Google Scholar]
  16. W. Zhang, F. Jin, G.G Zhang, B.C. Zhao, Y.Q. Hou. Aero-Engine Remaining Useful Life Estimation based on 1-Dimensional FCN-LSTM Neural Networks[A]. Proceedings of the 38th China Control Conference (4)[C]. 0, (2019). [Google Scholar]
  17. N. Hnoohom, A. Jitpattanakul, et al. Multi-sensor-based fall detection and activity daily living classification by using ensemble learning[C]. 111-115. (2018). [Google Scholar]

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