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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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
“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
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
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
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913
(1989) · neural information processing systems · Cited by 3,736
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
(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
Flagged first, then by citations
3 of the 54 references couldn’t be matched to a known paper.
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
Items 301–400 of 432
Human Activity Recognition Using LDA And Stochastic Configuration Networks
Yuan, G., Zang, C., & Zeng, P. (2022) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei57409.2022.9950204 · Cited by 2
Tan, Q., Qin, Y., Tang, R., Wu, S., & Cao, J. (2023) · Sensors · doi:10.3390/s23239613 · Cited by 2
Toward a holistic elderly-centred behaviour monitoring solution: Achievements and Opportunities
Naser, A., Lotfi, A., Pourabdollah, A., & Bird, J. J. (2023) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3594806.3596565 · Cited by 1
A Deep Learning Techniques for Brain Tumor Severity Level (K-CNN-BTSL) Using MRI Images
Saravanan, M., Sellamuthu, S., Bhardwaj, S., Mishr, C., & Parthasarathy, R. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-12419-8_14 · Cited by 1
A Hybrid CNN-LSTM Deep Neural Network Model for Efficient Human Activity Recognition
Maurya, D. N., Arora, D., & Singh, C. P. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-4533-3_31 · Cited by 1
Deep - learning based Human Activity Detection Model
Raveen, R., Rufai, S. Z., Haq, I. U., & Shah, H. A. (2024) · International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET) · doi:10.1109/i3ceet61722.2024.10993627 · Cited by 1
Enhanced Human Action Recognition Using CNN and DCGAN Preprocessing on the KTH Dataset
Narooka, P., Gupta, A., Tak, P., Goswami, M. P., Palit, R., & Keshan, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2724-0_13 · Cited by 1
Human activity recognition and behavioural prediction: a comprehensive systematic review
Gisha, G. S., Thangavel, M., & Udayan, J. D. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-21047-y · Cited by 1
Khalilipour, N., Tajik, R., Abbasi, A., Mantashloo, Z., & Aziminia, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110962 · Cited by 1
A two-phase hybrid clustering framework exploring transitional activities in HAR
Woo, M., Harby, A. A., Zulkernine, F. H., & Abdulsalam, H. M. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00503-6 · Cited by 2
Human Activity Recognition and Prediction using LSTM and Transformer
Soman, S. J., & Jeyaraj, J. R. A. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11170756 · Cited by 1
Liu, M., Liu, W., Yang, C., Zhu, W., Qiu, X., & Li, J. (2025) · International Conference on Intelligent Perception and Pattern Recognition (IPPR) · doi:10.1109/ippr66507.2025.11198318 · Cited by 1
Human Movement Classification With a Garment-Integrated Knot-Based Strain Sensor
Bellisle, R. F., Ahlers, M., Huynh, A., McGaa, N., Newman, D. J., Webb, A. K., & Arquilla, K. S. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3622919 · Cited by 1
Wang, M., Shan, W., Zhang, R., Liu, G., Chen, J., & Yang, Y. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02120-3 · Cited by 1
A systematic review of deep learning-based models for elderly and human activity recognition
Dalal, M., & Mittal, P. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100879 · Cited by 1
Feng, C., Bačić, B., Li, W., & Xu, H. (2026) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2026.1754717 · Cited by 1
Sheng, M., Wang, S., Qi, P., Tang, Q., & Su, B. (2025) · International Conference on Advanced Computer Theory and Engineering · doi:10.1109/icacte66284.2025.11412104 · Cited by 1
Rotation-Robust Self-Supervised Learning for IMU-Based HAR With Multi-Scale Convolutional Backbones
Lim, G., & Yi, M.-K. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3682243 · Cited by 1
Hok, P., Liu, R., Peng, Y., & Ding, Y. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3685178 · Cited by 1
A novel multi-scale attention network for human activity recognition using wearable sensor data
Zhou, W., Yuan, H., Cao, L., Chen, X., & Dong, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110351 · Cited by 1
Sheng, M., Wang, S., Ge, Z., Qi, P., Tang, Q., & Su, B. (2026) · Symmetry · doi:10.3390/sym18050823 · Cited by 1
CNN–GRU-Based High-Resolution Rainfall Measurement Using Download Signals of 5G Cellular Network
Mao, Y., Wang, J., Fiallos-Salguero, M., & Fiallos-Salguero, M. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3704239 · Cited by 1
A public domain dataset to recognize driver entry into and exit from a car using smartphone sensors
Hirawat, A., Taterh, S., & Sharma, T. K. (2021) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-021-01194-9 · Cited by 1
Salim, B. W., Hussan, B. K., Ageed, Z. S., & Zeebaree, S. R. M. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.037514 · Cited by 1
YU, F., Zheng, J., Yu, L., XIAO, H., Chen, Q., & ZHANG, D. (2023) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519423500859 · Cited by 1
Machine Learning Techniques for Human Activity Recognition Using Wearable Sensors
Das, M., Pundir, V., Mohindru, V., Garg, K. D., & Narang, S. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-2271-0_43 · Cited by 1
Human Identification Based on Gaits Analysis Using Sensors-Based Data Bands
Zhu, R., Liang, D., Zhang, Y., & Zhaohong, J. (2023) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict57916.2023.10244981 · Cited by 1
Ekambaram, D., & Ponnusamy, V. (2023) · AIP conference proceedings · doi:10.1063/5.0177963 · Cited by 1
Two-Branch Neural Network Using Two Data Types for Human Activity Recognition
Moreau, P., Durand, D., Bosche, J., & Lefranc, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3332290 · Cited by 1
Yan, D., Li, L., Liu, Y., Lin, X., Guo, L., & Chao, S. (2023) · Applied Sciences · doi:10.3390/app132312602 · Cited by 1
Human Activity Recognition Algorithm Based on ACNN-GRU
Wang, Y. (2023) · Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) · doi:10.1109/arace60380.2023.00016 · Cited by 1
Ghalan, M., & Aggarwal, R. K. (2023) · Evergreen · doi:10.5109/7160930 · Cited by 2
Exploring Dynamic Scenes: 3D CNN-Based Activity Recognition
Sharma, S., & Anjum, M. (2024) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito61523.2024.10522225 · Cited by 1
Enhancing Pedestrian Safety: Predicting Movements with Deep Learning Models
Farzana, F., & Valles, D. (2024) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot61789.2024.10579012 · Cited by 1
Human activity recognition based on improved convolutional neural network
Yang, S., Zhang, C., Zhong, P., Jing, M., Liu, M., & Hu, F. (2024) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1117/12.3031216 · Cited by 1
Smartphone Gait Authentication Based on Activity Recognition Task in Unconstrained Environment
Cheng, D., Mao, R., Hao, Q., Wang, Y., & Li, J. (2024) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc61673.2024.10733706 · Cited by 1
XAttentionHAR Ensemble: Leveraging Cross-Modal Attention for Enhanced Activity Recognition
Sahni, S., Jain, S., & Saritha, S. K. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424500265 · Cited by 1
Yan, S., LI, M., & PANG, X. (2025) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519425400421 · Cited by 1
Semantic Template Recognition of Human Activities in Wearable Sensor Data Using Siamese Network
Lattanzi, E., Contoli, C., Calisti, L., Fabrizio, G. D., Kania, N., & Peretti, S. (2025) · IEEE Access · doi:10.1109/access.2025.3561697 · Cited by 2
Human Activity Recognition Model Capable of Handling Various Input Waveforms
Hasegawa, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6606-5_1 · Cited by 1
Intelligent routing for human activity recognition in wireless body area networks
Selem, E., & Soliman, M. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-12114-3 · Cited by 1
Bhuyan, B. P., & Garg, S. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch004 · Cited by 1
Multi-Input CNN-LSTM for End-to-End Indian Sign Language Recognition
Gupta, R. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch008 · Cited by 1
Recent Advancements in Design and Implementation of Automated Sign Language Recognition Systems
Siddineni, B., & Manikandan, V. M. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch005 · Cited by 1
Gao, L., & Konomi, S. (2022) · Sensors · doi:10.3390/s23010113 · Cited by 1
Withdrawn: Leveraging LDA Feature Extraction to Augment Human Activity Recognition AccuracyPreprint
Vazan, M., Sharifi, E., Farahani, H., & Madadi, S. (2024) · Preprints.org · doi:10.20944/preprints202406.1528.v1 · Cited by 1
Nandakishora, Y., Christydass, S. P. J., Bhargav, K., & Kumar, S. S. (2025) · Wiley eBooks · doi:10.1002/9781394302895.ch8 · Cited by 1
Usad: End-to-End Human Activity Recognition Via Diffusion Model with Spatiotemporal AttentionPreprint
Xiao, H., Yu, Y., Li, J., Yang, Z., Tang, H., Liu, H., & Li, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5362912 · Cited by 1
A Six-Stream CNN Fusion-Based Human Activity Recognition on RGBD Data
Verma, K. K., & Singh, B. M. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch007
Lightweight ConvNet Model for American Sign Language Hand Gesture Recognition
Tiwari, S. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch009
Probabilistic Cascading Classifier for Energy-Efficient Activity Monitoring in Wearables
Pedram, M., Sah, R. K., Rokni, S. A., Nourollahi, M., & Ghasemzadeh, H. G. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3175881
Indoor Contact Awareness on Spatiotemporal Analytics with Smartphone-Based Pedestrian Dead Reckoning
Gao, L., & Konomi, S. (2022) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3524458.3547233
Online Personalisation of Deep Mobile Activity Recognisers
Milling, M., Aslan, I., Berghofer, M., Mallol-Ragolta, A., Kunwar, U., & Schuller, B. W. (2022) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3558884.3558896
An Extensive Study on HAR Systems to Recognize Daily Activities using Deep Learning Approaches
Tippani, G., & Gampala, V. (2022) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs55517.2022.10029206
Dong, X., Liu, C., Dong, S., Wang, S., & Huang, Q. (2023) · Research Square · doi:10.21203/rs.3.rs-2788705/v1
Quality control algorithm of wind speed monitoring data along high-speed railway
Ye, Y., Xiong, X., Cui, Y., & Yang, F. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1160302
A PSL-based Approach to Human Activity Recognition in Smart Home Environments
Li, Y. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140475
Khan, M. H., Shabbir, M., Li, J., Naseem, S., Mahmood, T., & Ahmad, F. (2023) · Research Square · doi:10.21203/rs.3.rs-3100158/v1
Batool, M., Jalal, A., Alazeb, A., Mudawi, N. A., Alshehri, M. S., Almakdi, S., Almujally, N. A., Alotaibi, S. S., & Algarni, A. M. (2023) · Research Square · doi:10.21203/rs.3.rs-3337380/v1
Gait Based Human Activity Recognition using Hybrid Neural Networks
Asokan, R., Pathmanaban, V., Shudhan, V. S., & Abirami, S. (2023) · International Conference on Advanced Computing · doi:10.1109/icoac59537.2023.10249714
Face Emotion Detection for Autism Children Using Convolutional Neural Network Algorithms
Umamaheswari, K. M., & Vignesh, M. T. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-12419-8_10
Unsupervised Human Activity Recognition Approach in Multi-User HouseholdsPreprint
Gómez-Ramos, R., Domingo, J. D., Zalama, E., & Gómez‐García‐Bermejo, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4586324
Multi-Phase Neural Networks with Ensemble Learning Model to Monitor Human Health ActivitiesPreprint
Dhar, J., Rana, K., Goyal, P., Alavi, A., Rana, R. K., Vo, Q. B., Mishra, S., & Mistry, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4594345
Helmi, A. M., Al-qaness, M., Dahou, A., Toureche, N., & Elaziz, M. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4705410
Girdhar, P., Johri, P., Virmani, D., Khalaf, O. I., Hajjej, F., Wong, W., & Algburi, S. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4715646
Optimizing End-to-End Sensor-Based Human Activity Recognition Through Multi-Attention InteractionPreprint
Yu, Y., Wang, H., Wang, J., Yan, M., Han, X., Wu, D., Shen, Q., & Liu, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4777248
Dehury, M. K., Gupta, R. K., & Kundu, T. (2023) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-6684-9576-6.ch003
Recent Trends in Video Surveillance System in Dense Environment: - A Review Paper
Tyagi, E. A. H. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i9.10082
3D Visualization Indoor Positioning System with Sequence Feature Enhancement Transformer Network
Yu, C., Cai, H., & Du, C. (2023) · International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) · doi:10.1109/aihcir61661.2023.00042
Zhai, Y., Mudassar, M., & Zhu, L. (2024) · SpringerBriefs in computer science · doi:10.1007/978-981-97-6998-8_4
Sain, M. K., Laskar, R. H., Singha, J., & Saini, S. (2024) · International Journal of Computers and Applications · doi:10.1080/1206212x.2024.2427287
Spatial-Temporal Network Model for Action Recognition Based on Keyframe Extraction
Zhao, D., Zhao, H., Yao, K., Meng, J., & Liang, M. (2024) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs63941.2024.10800578
Yan, D., Li, R., Xiong, W., & Huang, X. (2025) · Physica Scripta · doi:10.1088/1402-4896/adab43
Boulahia, M. A., Benharzallah, S., Titouna, F., & Dilekh, T. (2024) · International Conference on Innovative and Intelligent Information Technologies (IC3IT) · doi:10.1109/ic3it63743.2024.10869423
Zeng, J., Xu, H., Gao, F., Sun, F., Wang, X., Li, X., Li, Y., Li, X., & Ai, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5152077
A hybrid GRU-CRF framework for enhancing English tokenization efficiency and accuracy
Xu, T. (2025) · International Conference on Big Data, Computational Intelligence, and Applications (BDCIA) · doi:10.1117/12.3059114
Bayesian and Genetic Optimization for Human Activity Recognition with CNN and LSTM
Sultana, S., Rahman, T., Khatun, H., Sultana, N., Islam, M. R., & Ahmed, N. (2024) · International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM) · doi:10.1109/iipem62726.2024.10925732 · Cited by 1
Harnessing Wi-Fi CSI for Enhanced Human Activity Recognition: A Feature-Driven Approach
Aparna, B. P., & Poulose, A. (2025) · Emerging Technologies for Intelligent Systems (ETIS) · doi:10.1109/etis64005.2025.10961699
Thakur, D., Jadhav, A., Malviya, L., & Bhudolia, K. N. (2025) · Springer tracts in human-centered computing · doi:10.1007/978-981-96-1761-6_30
Hybrid deep learning model for human activity recognition using smartphone and smart home data
Nadia, A., Sabri, L., & Kazar, O. (2025) · Journal of Ambient Intelligence and Smart Environments · doi:10.1177/18761364251339587
Mondal, P., Sarkar, A., & Sarkar, R. (2025) · Progress in Artificial Intelligence · doi:10.1007/s13748-025-00381-0
Enhanced Feature Extraction for Accurate Human Action Recognition
Elgaml, T., Saudi, A. S., & Taha, M. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160691
Liu, Z. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.mh24964
Zero Trust for Secure Wireless Sensor Networks
Vikas, V., Wahi, C., Sagar, B. B., & Manjul, M. (2025) · International Conference on Networks and Cryptology (NETCRYPT) · doi:10.1109/netcrypt65877.2025.11102154
Block-HAR: Trusted Human Activity Recognition Framework for 5G-assisted Smart Healthcare
Bhattacharya, P., Chatterjee, S., Dutt, P. K., Tikadar, S., & Gadekallu, T. R. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100752
End-to-End Human Activity Recognition via Diffusion Model with Spatiotemporal AttentionPreprint
Xiao, H., Yu, Y., Li, J., Yang, Z., Liu, H., & Li, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5412923
Sain, M. K., Laskar, R. H., Singha, J., & Saini, S. (2025) · Robotica · doi:10.1017/s0263574725102312 · Cited by 1
Anjani, A. K. (2025) · Research Square · doi:10.21203/rs.3.rs-7481774/v1
Optimized Insurance Sales Predictions with SaleNet-CG Powered by CNN-GRU Hybrid Framework
Jayanth, H., Sulaiman, S. K., Al-Assal, J. R. R., Kiran, A., Kotteswaran., D., & Saravanakumar, R. (2025) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns65743.2025.11168816
Reconstructing a Full-body Model from a Limited Set of Upper-Limb Motion Data
Obukhov, A. D., & Teselkin, D. V. (2025) · Advanced Engineering Research · doi:10.23947/2687-1653-2025-25-3-221-232
Personalization of Human Activity Recognition with Cross-Conditioned Diffusion Models
Hasegawa, T., & Sakai, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4094-5_16
Leveraging LDA feature extraction to augment human activity recognition accuracy
Vazan, M., Sharifi, E., Farahani, H., & Madadi, S. (2025) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2025.104453
Wearable Multi-Sensor System for Human Activity Recognition using Fusion-based Deep Learning
Mitra, P., Quadri, H., Bella, K. P., Kiron, V., Maity, M., & Priyakumar, D. (2025) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon66021.2025.11254669
Zhang, X., Wang, Y., & Deng, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-32859-1
AI-Based Techniques for Human Activity Recognition in Various Domains
Shobitha, R. R., & Anbalagan, E. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5604-2_8
Interaction Modes for Virtual Digital Humans in Museums Under Convolutional Neural Networks
Zhu, E., & Wang, L. (2026) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.398929
GCGConv: Enhancing Human Activity Recognition with Global Context Graph Convolutions
Yuan, Q., & Verma, P. (2025) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60686.2025.11342548
Can deep learning trigger alerts from mobile-captured images?
Sarkar, P., Saha, D. D. V., & Saha, M. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21278-7
Chen, H., Wang, C., Zhi, D., & Yang, Q. (2026) · Displays · doi:10.1016/j.displa.2026.103415
IMUDistill: Knowledge Transfer for Enhancing Low Precision IMU Performance
Mishra, S., Kumar, H., Ko, K. W., & Har, D. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3674623
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
