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A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
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“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]
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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
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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).
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Tokas, P., Semwal, V. B., & Jain, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20352-2 · Cited by 10
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Zheng, W., & Arakawa, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3598088 · Cited by 11
Chen, L., Luo, Y., Peng, L., Hu, R. J., Zhang, Y., & Miao, S. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04997-4 · Cited by 10
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Tarekegn, A. N., Sajjad, M., Cheikh, F. A., Ullah, M., & Muhammad, K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3319353 · Cited by 12
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Zolfaghari, S., Massa, S. M., & Riboni, D. (2023) · Electronics · doi:10.3390/electronics12091969 · Cited by 9
Barua, A., Jiang, X., & Fuller, D. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01213-3 · Cited by 11
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Self-citationElhoseny, M., Abdel-Salam, M., Yang, J., & El‐Hasnony, I. M. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3582598 · Cited by 9
Chen, Q., Lu, D., Feng, W., Ye, C., Qiu, L., Pan, J., & Li, J. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3584138 · Cited by 10
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Barua, A., Fuller, D., Musa, S. B., & Jiang, X. (2022) · Biosensors · doi:10.3390/bios12070549 · Cited by 9
Ghazi, M. E., & Aknin, N. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141032 · Cited by 9
Yan, L., Xiu, H., & Wei, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3327723 · Cited by 8
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Xu, Z., Liu, M., Li, T., Liu, X., Cheng, X., & Chen, S. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3555792 · Cited by 8
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Han, H., Kim, G., Choi, S., Basu, A. S., & Yoon, S. W. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3332897 · Cited by 9
Cheng, X., Bao, B., Cui, W., Liu, S., Zhong, J., Cai, L., & Yang, H. (2023) · Sensors · doi:10.3390/s23125482 · Cited by 7
Kiani, M., Shafaghi, H., Ghalati, A. G., & Moaiyeri, M. H. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3568970 · Cited by 7
Zhang, W., Ye, N., Zhang, L., Liu, X., Wu, H., & Song, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3639422 · Cited by 7
A lightweight deep learning with feature weighting for activity recognition
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Kushwaha, A., Srivastava, P., & Khare, A. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7571 · Cited by 7
Wu, J., & Liu, Q. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11385-z · Cited by 7
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Xie, Y., Yao, Y., & Li, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-96462-0 · Cited by 6
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Ayub, M., & El-Alfy, E.-S. M. (2025) · IEEE Access · doi:10.1109/access.2025.3631392 · Cited by 5
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Marhraoui, Y. E., Bounhar, O., Boukallel, M., Anastassova, M., Bouilland, S., & Ammi, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3519225 · Cited by 7
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Daily life human activities recognition using a novel AR‐DenseNet
Ghalan, M., & Aggarwal, R. K. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7142 · Cited by 4
Lu, L., & Deng, T. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109141 · Cited by 3
Infectious disease prediction model based on optimized deep learning algorithm
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Brery, A. F., Gallardo-Antolín, A., Fakhry, M., & González‐Carrasco, I. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132415 · Cited by 3
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Islam, S. M. M., & Talukder, K. H. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101398 · Cited by 4
Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data
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Sharma, H. K., & Choudhury, T. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch003 · Cited by 3
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Human Activity Recognition with smartphone sensors data using CNN-LSTM
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STMultiple: Sparse Transformer Based on RFID for Multi-Object Activity Recognition
Shen, S., Yang, M., Hou, X., Yang, L., Chen, S., Dong, W., Yu, B. W., & Wang, Q. (2023) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194023410073 · Cited by 3
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Arab, A., Schmidt, A., & Aufderheide, D. (2023) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais60147.2023.10346052 · Cited by 3
Ridwan, R., Hermaliani, E. H., & Ernawati, M. (2024) · Computer Science (CO-SCIENCE) · doi:10.31294/coscience.v4i1.2990 · Cited by 3
Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt
Verma, U., Tyagi, P., & Aneja, M. K. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad43b9 · Cited by 3
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring
Gahtan, B., Funk, S., Ketko, I., Kodesh, E., Kuflik, T., & Bronstein, A. M. (2025) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi64645.2025.00075 · Cited by 3
Hazra, S., Pratap, A. A., & Nandy, A. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3222087 · Cited by 3
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Sengupta, R., Polian, I., & Hayes, J. P. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3540564 · Cited by 2
Unsupervised Deep Clustering for Human Behavior Understanding
Yang, W., Sui, Y., Zhang, Y., & Xia, S. (2025) · International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems · doi:10.1145/3722570.3726885 · Cited by 2
Zhang, J., Peng, C., Liu, Z., Feng, L., Wu, Y., Dong, Y., & Xu, D. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3572220 · Cited by 3
Human activity recognition (HAR): techniques, applications, and future directions
Rani, M., & Kumar, M. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01042-8 · Cited by 2
Zhang, Y., Xiang, C., & Zhang, Y. (2026) · IET Biometrics · doi:10.1049/bme2/8129974 · Cited by 2
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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