The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data
Abstract
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3-D CNN) based method for fall detection is developed, which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2-D CNN could only encode spatial information, and the employed 3-D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a long short-term memory (LSTM) based spatial visual attention scheme is incorporated. Sports dataset Sports-1 M with no fall examples is employed to train the 3-D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
(2014) · Cited by 33,419
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Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100303 · Cited by 375
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Udriștoiu, A. L., Cazacu, I. M., Gruionu, L. G., Gruionu, G., Iacob, A. V., Burtea, D. E., Ungureanu, B. S., Costache, M. I., Constantin, A., Popescu, C. F., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251701 · Cited by 63
Convolutional neural network and long short-term memory models for ice-jam predictions
Madaeni, F., Chokmani, K., Lhissou, R., Homayouni, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Qin, X., Wang, J., Chen, Y., Lu, W., & Jiang, X. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3552434 · Cited by 55
Metadata
- Authors
- Lu, N., Wu, Y., Feng, L., & Song, J.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2019-01
- Volume
- 23
- Issue
- 1
- Pages
- 314-323
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2909645133
- Cited by
- 339
- References
- 54
- FWCI
- 16.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lu, N., Wu, Y., Feng, L., & Song, J. (2019). Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2018.2808281
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