The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
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Citations
Cites
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
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 [41], the authors proposed an offline HAR system that uses a smartphone with a built-in three-axis accelerometer sensor.”
Background(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959
Human activity recognition with smartphone sensors using deep learning neural networks
Ronao, C. A., & Cho, S.-B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.032 · Cited by 1,215
Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35395-6_30 · Cited by 971
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
Ignatov, A. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.027 · Cited by 814
Transition-Aware Human Activity Recognition Using Smartphones
Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., & Anguita, D. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.07.085 · Cited by 740
A robust human activity recognition system using smartphone sensors and deep learning
Hassan, M. M., Uddin, M. Z., Mohamed, A., & Almogren, A. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.029 · Cited by 656
Classification Accuracies of Physical Activities Using Smartphone Motion Sensors
Wu, W., Dasgupta, S., Ramirez, E. E., Peterson, C., & Norman, G. J. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2208 · Cited by 316
Cited by
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
Ramanujam, E., Perumal, T., & Padmavathi, S. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3069927 · Cited by 455
Multi-input CNN-GRU based human activity recognition using wearable sensors
Dua, N., Singh, S. N., & Semwal, V. B. (2021) · Computing · doi:10.1007/s00607-021-00928-8 · Cited by 432
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Challa, S. K., Kumar, A., & Semwal, V. B. (2022) · The Visual Computer · doi:10.1007/s00371-021-02283-3 · Cited by 338
Multiscale Deep Feature Learning for Human Activity Recognition Using Wearable Sensors
Tang, Y., Zhang, L., Min, F., & He, J. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3161812 · Cited by 224
Metadata
- Authors
- Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z.
- Venue
- Mobile Networks and Applications
- Published
- 2019-12-30
- Volume
- 25
- Issue
- 2
- Pages
- 743-755
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- OpenAlex
- W2998376881
- Cited by
- 594
- References
- 42
- FWCI
- 28.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep Learning Models for Real-time Human Activity Recognition with Smartphones. https://app.cochat.ai/papers/10.1007/s11036-019-01445-x
- The paper
- Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2020). Deep Learning Models for Real-time Human Activity Recognition with Smartphones. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01445-x
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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.
