1 |
M. Tentori and J. Favela, "Activity-aware computing for healthcare", Pervasive Computing IEEE, vol. 7, pp. 51-57, 2008.
DOI
|
2 |
S. Qiu, H. Zhao, N. Jiang, Z. Wang, L. Liu, Y. An, H. Zhao, X. Miao, R. Liu, G. Fortino, Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges, Information Fusion, Volume 80.
|
3 |
K. K. Htike, O. O. Khalif a, H. A. Mohd R amli and M. A. M. Abushariah, "Human activity recognition for video surveillance using sequences of postures," The Third International Conference on e-Technologies and Networks for Development (ICeND2014), pp. 79-82, 2014.
|
4 |
A. Jalal and M. A. Zeb, Security Enhancement for E-learning portal, Int. J. Comput. Sci. Netw. Security 8 (2008), no. 3, 41- 45.
|
5 |
J. Wang, Y. Chen, S. Hao, X. Peng, & L. Hu, "Deep learning for sensor-based activity recognition: a survey. Pattern Recogn". Lett. 119, 3-11 (2019).
|
6 |
L.F. Yeung, Z. Yang, K.C.C. Cheng, D. Du. and R.K.Y. Tong, Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2. Gait & Posture, 87, pp. 19-26. 2021.
DOI
|
7 |
Y. Zhao," Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors," 1-5, 2018.
|
8 |
J. Guo, K. Tian, K. Ye and C. -Z. Xu, "MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Extraction," 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3605-3611, 2021.
|
9 |
J. F. Kolen; S. C. Kremer, "Gradient Flow in Recurrent Nets: The Difficulty of Learning LongTerm Dependencies," in A Field Guide to Dynamical Recurrent Networks, IEEE, pp. 237-243, 2001.
|
10 |
H. Sepp & S. Jurgen. Long Short-term Memory. Neural computation. 9. 1997.
|
11 |
Y. Zhao, Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors. Math. Probl. Eng. 2018, 2018.
|
12 |
J. Wang, Y. Chen, S. Hao, X. Peng, L. Hu, Deep learning for sensor-based activity recognition: A survey, Pattern Recognition Letters, Volume 119, Pages 3-1 2019.
DOI
|
13 |
Y. Bengio, P. Simard and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," in IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166, March 1994.
DOI
|
14 |
FJ, Ordonez, D. Roggen Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors. 2016
|