The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A robust human activity recognition system using smartphone sensors and deep learning
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Citations
Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 1×
“In [33], the authors proposed an offline HAR system where a smartphone with built-in triaxial accelerometer sensor was used.”
BackgroundA survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Cited by
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-019-01445-x · Cited by 594
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“Researchers have proposed various algorithms to extract features from sensor signals and to perform activity classification using machine-learned models like K Nearest Neighbor (KNN) and SVM [123, 149, 168, 193, 254, 265, 270, 290, 313, 319].”
MethodInfluentialIntelligent video surveillance: A review through deep learning techniques for crowd analysis
Sreenu, G., & Saleem Durai, M. A. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0212-5 · Cited by 460
Metadata
- Authors
- Hassan, M. M., Uddin, M. Z., Mohamed, A., & Almogren, A.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 81
- Pages
- 307-313
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2770265759
- Cited by
- 656
- References
- 3
- FWCI
- 23.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Hassan, M. M., Uddin, M. Z., Mohamed, A., & Almogren, A. (2018). A robust human activity recognition system using smartphone sensors and deep learning. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.11.029
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
