The DOI matches its Crossref record; 51 of the 54 references it cites were found in a registry or an index.
Multi-input CNN-GRU based human activity recognition using wearable sensors
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Dua et al. [248] proposed a model with CNN combined with GRU and obtained an accuracy of 96.20%, 97.21%, and 95.27% on UCI-HAR, WISDM [81], and PAMAP2 [91] datasets, respectively.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [248]
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Learning long-term dependencies with gradient descent is difficult
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On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251
The Handbook of brain theory and neural networks
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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
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
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LSTM Fully Convolutional Networks for Time Series Classification
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Research works printed but not linked: 3
- Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint http://arxiv.org/abs/1412.3555 · arXiv
- Cui Z, Chen W, Chen Y (2016) Multi-scale convolutional neural networks for time series classification. arXiv preprint http://arxiv.org/abs/1603.06995 · arXiv
- Hammerla NY, Halloran S, Plötz T (2016) Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint http://arxiv.org/abs/1604.08880 · arXiv
Cited by
Sorted by citations
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 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
“[86] proposed a model with CNN combined with GRU and obtained accuracy of 96.”
MethodZhao, Z., Yun, S., Jia, L., Guo, J., Meng, Y., He, N., Li, X., Shi, J., & Liu, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105982 · Cited by 395
SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing
Yang, J., Chen, X., Zou, H., Lu, C. X., Wang, D., Sun, S., & Xie, L. (2023) · Patterns · doi:10.1016/j.patter.2023.100703 · Cited by 260
Semwal, V. B., Gaud, N., Lalwani, P., Bijalwan, V., & Alok, A. K. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09979-x · Cited by 191
Self-citation
Metadata
- Authors
- Dua, N., Singh, S. N., & Semwal, V. B.
- Affiliations
- National Institute of Technology Jamshedpur (IN)
- Maulana Azad National Institute of Technology (IN)
- Corresponding author
- Dua, Nidhi
- Venue
- Computing
- Published
- 2021-03-10
- Volume
- 103
- Issue
- 7
- Pages
- 1461-1478
- ISSN
- 0010-485X (ISSN-L)
- 1436-5057
- OpenAlex
- W3133590696
- Cited by
- 432
- References
- 54
- Citations by year
- 2026: 44 · 2025: 82 · 2024: 96 · 2023: 122 · 2022: 70 · 2021: 18
- FWCI
- 27.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computing (publishedVersion, public-domain)
- Funders
- Science and Engineering Research Board (ECR/2018/000203)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, wearable sensors, CNN-GRU, convolutional neural networks, gated recurrent unit, end-to-end feature extraction, accelerometer, gyroscope, time series classification, UCI-HAR, WISDM, PAMAP2
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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