The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The accuracy on UCI-HAR, WISDM [81], and PAMAP2 [91] datasets achieved 96.37%, 96.05%, and 94.29%, respectively.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [247]
In the citing paper
“For example, Challa et al. [247] proposed a hybrid of CNN and bidirectional long short-term memory (BiLSTM).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [247]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646
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
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959
LSTM Fully Convolutional Networks for Time Series Classification
Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017) · IEEE Access · doi:10.1109/access.2017.2779939 · Cited by 1,560
Introducing a New Benchmarked Dataset for Activity Monitoring
Reiss, A., & Stricker, D. (2012) · 2012 16th International Symposium on Wearable Computers · doi:10.1109/iswc.2012.13 · Cited by 1,456
Research works printed but not linked: 2
- Wang, K., He, J., Zhang, L.: Sequential weakly labeled multi-activity localization and recognition on wearable sensors using recurrent attention networks. arXiv e-prints, pp. arXiv-2004 (2020).
- Hammerla, N.Y., Halloran, S. and Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint (2016).
Cited by
Sorted by citations
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
“[55] proposed a hybrid of CNN and bidirectional long short-term memory (BiLSTM).”
BackgroundDeep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Foumani, N. M., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3649448 · Cited by 328
Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 228
Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M. F., & Singh, P. K. (2022) · Biosensors · doi:10.3390/bios12060393 · Cited by 126
Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model
Afsar, M. M., Saqib, S., Aladfaj, M., Alatiyyah, M. H., Alnowaiser, K., Aljuaid, H., Jalal, A., & Park, J. (2023) · IEEE Access · doi:10.1109/access.2023.3239692 · Cited by 120
Metadata
- Authors
- Challa, S. K., Kumar, A., & Semwal, V. B.
- Affiliations
- National Institute of Technology Jamshedpur (IN)
- Maulana Azad National Institute of Technology (IN)
- Corresponding author
- Challa, Sravan Kumar
- Venue
- The Visual Computer
- Published
- 2021-08-20
- Volume
- 38
- Issue
- 12
- Pages
- 4095-4109
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3193597430
- Cited by
- 338
- References
- 52
- Citations by year
- 2026: 44 · 2025: 85 · 2024: 78 · 2023: 96 · 2022: 34 · 2021: 1
- FWCI
- 18.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Visual Computer (publishedVersion, public-domain)
- Funders
- Department of Science and Technology (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, automatic feature extraction, time series classification, long-range dependencies, smartphone sensor data, WISDM, UCI-HAR, 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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