Learning hierarchical time series data augmentation invariances via contrastive supervision for human activity recognition
Integrity signals
Registry
Cites
Statistics
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?
Cheng et al. show that supervised learning coupled with contrastive losses at intermediate layers prevents information loss due to augmentation.
“They show that supervised learning, coupled with contrastive losses at intermediate layers, prevents information loss due to augmentation.”
From Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task (2025) · cites this paper as [32]
Leveraging the temporal nature of IMU signals allows for the application of augmentation methods grounded in signal processing.
“In sensor-based HAR leveraging the temporal nature of IMU signals allows for the application of augmentation methods grounded in signal processing [32,34,99,132,141,178,193].”
From Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task (2025) · cites this paper as [32]
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515 · see citing works
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.07733 · Cited by 3,846 · see citing works
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725 · see citing works
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664 · see citing works
Supervised Contrastive Learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., & Krishnan, D. (2020) · arXiv · doi:10.48550/arxiv.2004.11362 · Cited by 3,569 · see citing works
Deeply-Recursive Convolutional Network for Image Super-Resolution
Kim, J., Lee, J. K., & Lee, K. M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.181 · Cited by 3,122 · see citing works
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 · see citing works
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 · see citing works
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014 · see citing works
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668 · see citing works
Deeply-Supervised NetsPreprint
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5185 · Cited by 1,661 · see citing works
Big Self-Supervised Models are Strong Semi-Supervised LearnersPreprint
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10029 · Cited by 1,480 · see citing works
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 · see citing works
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 · see citing works
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 998 · see citing works
(2015) · Cited by 989 · see citing works
Artificial Intelligence and Statistics
Ziegel, E. R., & Gale, W. G. (1989) · Technometrics · doi:10.2307/1270393 · Cited by 758 · see citing works
Time Series Data Augmentation for Deep Learning: A Survey
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/631 · Cited by 727 · see citing works
Understanding the Behaviour of Contrastive Loss
Wang, F., & Liu, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00252 · Cited by 698 · see citing works
Deep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Ye, M., Zhang, X., Yuen, P. C., & Chang, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00637 · Cited by 629 · see citing works
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · doi:10.20944/preprints201706.0033.v1 · Cited by 596 · see citing works
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596 · see citing works
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Guan, Y., & Plötz, T. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3090076 · Cited by 511 · see citing works
Yadav, S. K., Tiwari, K., Pandey, H. M., & Akbar, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106970 · Cited by 328 · see citing works
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
Li, F., Shirahama, K., Nisar, M., Köping, L., & Grzegorzek, M. (2018) · Sensors · doi:10.3390/s18020679 · Cited by 302 · see citing works
Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2016) · 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2016.7516235 · Cited by 269 · see citing works
AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition
Ma, H., Li, W., Zhang, X., Gao, S., & Lu, S. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/431 · Cited by 266 · see citing works
Zhang, H., Xiao, Z., Wang, J., Li, F., & Szczerbicki, E. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2949715 · Cited by 199 · see citing works
Deep Activity Recognition Models with Triaxial Accelerometers
Alsheikh, M. A., Selim, A., Niyato, D. T., Doyle, L., Lin, S., & Tan, H.-P. (2015) · DR-NTU (Nanyang Technological University) · Cited by 191 · see citing works
Attention induced multi-head convolutional neural network for human activity recognition
Khan, Z. N., & Ahmad, J. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107671 · Cited by 187 · see citing works
Zeng, M., Gao, H., Yu, T., Mengshoel, O. J., Langseth, H., Lane, I., & Liu, X. (2018) · Proceedings of the 2018 ACM International Symposium on Wearable Computers · doi:10.1145/3267242.3267286 · Cited by 185 · see citing works
Al‐qaness, M. A. A., Dahou, A., Elaziz, M. A., & Helmi, A. M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3165875 · Cited by 173 · see citing works
On attention models for human activity recognition
Murahari, V. S., & Plötz, T. (2018) · Proceedings of the 2018 ACM International Symposium on Wearable Computers · doi:10.1145/3267242.3267287 · Cited by 157 · see citing works
Contrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463506 · Cited by 150 · see citing works
Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448083 · Cited by 107 · see citing works
Deep Supervision with Additional Labels for Retinal Vessel Segmentation Task
Zhang, Y., & Chung, A. C. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_10 · Cited by 104 · see citing works
Deeply-Supervised Knowledge Synergy
Sun, D., Yao, A., Zhou, A., & Zhao, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00716 · Cited by 91 · see citing works
On the role of features in human activity recognition
Haresamudram, H., Anderson, D. V., & Plötz, T. (2019) · Proceedings of the 23rd International Symposium on Wearable Computers · doi:10.1145/3341163.3347727 · Cited by 90 · see citing works
2012 16th International Symposium on Wearable Computers
(2012) · doi:10.1109/iswc20335.2012 · Cited by 90 · see citing works
Semi-supervised convolutional neural networks for human activity recognition
Zeng, M., Yu, T., Wang, X., Nguyen, L. T., Mengshoel, O. J., & Lane, I. (2017) · 2017 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2017.8257967 · Cited by 85 · see citing works
Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition
Kim, E. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2972628 · Cited by 85 · see citing works
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Li, C., Zia, M. Z., Tran, Q.-H., Yu, X., Hager, G. D., & Chandraker, M. K. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.49 · Cited by 82 · see citing works
Sensor Data Augmentation by Resampling in Contrastive Learning for Human Activity Recognition
Wang, J., Zhu, T., Gan, J., Chen, L., Ning, H., & Wan, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3214198 · Cited by 81 · see citing works
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
Qian, H., Tian, T., & Miao, C. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539134 · Cited by 67 · see citing works
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Tang, C. I., Perez-Pozuelo, I., Spathis, D., & Mascolo, C. (2020) · arXiv · doi:10.48550/arxiv.2011.11542 · Cited by 67 · see citing works
Garcia-Gonzalez, D., Rivero, D., Fernández-Blanco, E., & Luaces, M. R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110260 · Cited by 66 · see citing works
Deep Dilated Convolution on Multimodality Time Series for Human Activity Recognition
Xi, R., Hou, M., Fu, M., Qu, H., & Liu, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489540 · Cited by 57 · see citing works
Asymmetric Residual Neural Network for Accurate Human Activity Recognition
Long, J., Sun, W., Yang, Z., & Raymond, O. I. (2019) · Information · doi:10.3390/info10060203 · Cited by 56 · see citing works
CAVIAR: Context-driven Active and Incremental Activity Recognition
Bettini, C., Civitarese, G., & Presotto, R. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105816 · Cited by 54 · see citing works
Zhang, L., Chen, X., Zhang, J., Dong, R., & Ma, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19809-0_1 · Cited by 54 · see citing works
Self-citationEvidential Reasoning With Hesitant Fuzzy Belief Structures for Human Activity Recognition
Dong, Y., Li, X., Dezert, J., Zhou, R., Zhu, C., Wei, L., & Ge, S. S. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3079495 · Cited by 50 · see citing works
Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition
Khaertdinov, B., Ghaleb, E., & Asteriadis, S. (2021) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom50583.2021.9439116 · Cited by 43 · see citing works
Cho, H., & Yoon, S. M. (2019) · Electronics Letters · doi:10.1049/el.2018.6117 · Cited by 32 · see citing works
A Comprehensive Review on Deep Supervision: Theories and ApplicationsPreprint
Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S. N., Garg, S., Alty, J., & Bai, Q. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2207.02376 · Cited by 16 · see citing works
Bravo, J. J. M. (2012) · Cited by 12 · see citing works
A Transformer Based Approach for Activity Detection
Sharma, G., Dhall, A., & Subramanian, R. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3551598 · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
An ensemble deep learning model for human activity analysis using wearable sensory data
Batool, S., Khan, M. H., & Farid, M. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111599 · Cited by 52 · see citing works
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
Wang, K., Ghafurian, M., Chumachenko, D., Cao, S. C., Butt, Z. A., Salim, S., Abhari, S., & Morita, P. P. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108340 · Cited by 30 · see citing works
Yao, M., Zhang, L., Cheng, D., Qin, L., Liu, X., Fu, Z., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3371462 · Cited by 25 · see citing works
Self-citationYang, Z., Li, Y., & Zhou, G. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3353209 · Cited by 22 · see citing works
Ashfaq, N., Khan, M. H., & Nisar, M. A. (2024) · Information · doi:10.3390/info15060343 · Cited by 21 · see citing works
Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review
Chen, H., Gouin-Vallerand, C., Bouchard, K., Gaboury, S., Couture, M., Bier, N., & Giroux, S. (2024) · IEEE Access · doi:10.1109/access.2024.3480814 · Cited by 23 · see citing works
Wearable sensors in Industry 4.0: Preventing work-related musculoskeletal disorders
Alenjareghi, M. J., Sekkay, F., Dadouchi, C., & Keivanpour, S. (2025) · Sensors International · doi:10.1016/j.sintl.2025.100343 · Cited by 20 · see citing works
An Improved Adaptive Radar Signal Sorting Algorithm Based on DBSCAN by a Novel CVI
Su, Y., Zhao, C., Gong, L.-F., Xu, X., & Yao, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3361221 · Cited by 19 · see citing works
Virtual Fusion With Contrastive Learning for Single-Sensor-Based Activity Recognition
Nguyen, D., Pham, C., & Le‐Khac, N. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3412397 · Cited by 22 · see citing works
Zheng, P., Zhang, A., Chen, J., Li, Q., & Yang, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3352425 · Cited by 13 · see citing works
Zou, H., Chen, Z.-J., Zhang, C., Yuan, A., Wang, B., Wang, L., Li, J.-Q., & Pan, Y.-Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3373444 · Cited by 13 · see citing works
Yu, J., Zhang, L., Cheng, D., Bu, C., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3412736 · Cited by 12 · see citing works
Self-citationTEM1Dformer: A Novel 1-D Time Series Deep Denoising Network for TEM Signals
Pan, D., Qi, T., Feng, G., Wang, H., Zhang, Z., & Wei, X. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3330468 · Cited by 11 · see citing works
Encoding human activities using multimodal wearable sensory data
Khan, M. H., Shafiq, H., Farid, M. S., & Grzegorzek, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125564 · Cited by 12 · see citing works
Guo, X., Kim, Y., Ning, X., & Min, S. D. (2025) · Sensors · doi:10.3390/s25020301 · Cited by 11 · see citing works
Fan, S., Tang, Y., Chi, H., Hou, D., Zhang, G., & Cao, Y. (2023) · Measurement · doi:10.1016/j.measurement.2023.113926 · Cited by 11 · see citing works
Yang, Z., Ma, J., Yang, K., Lin, H., Chen, H., Yang, R., & Chang, Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103657 · Cited by 8 · see citing works
Hassan, M. A., Kelsey, T., & Rahman, F. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298888 · Cited by 8 · see citing works
Yao, M., Cheng, D., Zhang, L., Liu, L., Song, S. J., Wu, H., & Song, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112444 · Cited by 8 · see citing works
Self-citationWaveHAR: Learning Wavelet Representation for Wearable Human Activity Recognition
Yao, M., Zhang, L., Cheng, D., Wu, H., & Song, A. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3571487 · Cited by 8 · see citing works
Self-citationLiu, X., Cheng, D., Fu, Z., Zhang, L., Wu, H., & Song, A. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3664424 · Cited by 12 · see citing works
Self-citationYao, M., Zhang, L., Cheng, D., Liu, J., Wang, L., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3412395 · Cited by 6 · see citing works
Self-citationSeeking optimal and explainable deep learning models for inertial-based posture recognition
Martins, D. R., Cerqueira, S. M., & Santos, C. P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112700 · Cited by 6 · see citing works
Convolutional neural network for gesture recognition human-computer interaction system design
Niu, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0311941 · Cited by 6 · see citing works
Long kernel distillation in human activity recognition
Yao, M., Cheng, D., Zhang, L., Li, W., Wu, H., & Song, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113397 · Cited by 6 · see citing works
Self-citationRen, J., Che, J., Gong, P., Wang, X., Li, X., Li, A., & Xiao, C. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108102 · Cited by 6 · see citing works
Sheng, T., & Huber, M. (2025) · Sensors · doi:10.3390/s25134032 · Cited by 5 · see citing works
Ashfaq, N., Aziz, Z., Khan, M. H., Nisar, M. A., & Khalid, A. (2025) · IEEE Access · doi:10.1109/access.2025.3577730 · Cited by 5 · see citing works
ActiFormer: Sign-Aware Linear Attention for Sensor-Based Human Activity Recognition
Sun, Q., Fu, Z., Zhang, L., Chen, G., Huang, W., Wu, H., & Song, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3633705 · Cited by 5 · see citing works
Self-citationAccurate graph classification via two-staged contrastive curriculum learning
Shim, S., Kim, J., Park, K.-H., & Kang, U. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296171 · Cited by 5 · see citing works
An, D., Pan, Z., Zhao, Q., Liu, W., & Liu, J. (2024) · Mathematics · doi:10.3390/math12131991 · Cited by 5 · see citing works
Zhang, C., Qin, H., Zhang, Y., Jiang, C., Zhang, D., & Deng, W. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110190 · Cited by 5 · see citing works
Ye, N., Zhang, L., Xiong, D., Wu, H., & Song, A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3458441 · Cited by 4 · see citing works
Self-citationLoDiHAR: A Low-Cost Distributed Human Activity Recognition System Based on RFID
Sun, X., Wang, Y., Zhang, C., Wang, Z., Shi, X., & Zheng, Y. (2024) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon64284.2024.10934903 · Cited by 3 · see citing works
Sultan, I., & Banday, M. T. (2024) · International Journal of Sensors Wireless Communications and Control · doi:10.2174/0122103279287156240218044819 · Cited by 3 · see citing works
Ariaeimehr, M., & Ravanmehr, R. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-21086-5 · Cited by 2 · see citing works
Human Activity Recognition Using Spatio-Temporal Dual Attention with Cross-Sensor Attention
Chandirakumar, M., Kanagarajah, T., Kalanantharasan, N., Wickramanayake, S., & Meedeniya, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228134 · Cited by 2 · see citing works
Comparative performance of machine learning models for the classification of human gait
Thakur, D., & Lalwani, P. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad17f9 · Cited by 2 · see citing works
Time-Series Representation Learning via Dual Reference Contrasting
Yu, R., Gong, Y., Wang, S., Si, J., Peng, X., Xu, B., & Lü, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679699 · Cited by 2 · see citing works
Alotaibi, F. A., Alnfiai, M. M., Al‐Wesabi, F. N., Duhayyim, M. A., Hilal, A. M., & Hamza, M. A. (2024) · Journal of Mathematics · doi:10.1155/2024/5551009 · Cited by 2 · see citing works
Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM
Wibowo, A. W., Sato-Shimokawara, E., & Kurnianingsih, K. (2024) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc63072.2024.10935279 · Cited by 2 · see citing works
AdaMaskNet: adaptive multi-scale masked kernels for enhanced sensor-based human activity recognition
Tang, J., Dong, C., & Li, M. (2025) · The Visual Computer · doi:10.1007/s00371-025-03943-4 · Cited by 1 · see citing works
Multi-scale Dynamic Preservation Mapping: A Time Series Data Augmentation Approach
Lu, Y., Yan, J., Zhang, W., & Huang, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4384-7_26 · Cited by 1 · see citing works
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 · see citing works
Zuo, H., Deng, J., Liu, Z., Tian, G., & Xiao, S. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424500241
Link Prediction in Social Networks Using the HTOA
Asef, F., Majidnezhad, V., & Feizi‐Derakhshi, M. (2025) · IEEE Access · doi:10.1109/access.2025.3586898
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
Li, Z., Tao, J., & Shou, D. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3652182
Spectral-adaptive masking and hierarchical contrastive learning for time series clustering
Wang, Z., Xu, X., Xu, S. L., Guo, N., Bian, X., Yao, S., Zhou, T., & Shen, Y. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x251413367
Du, Z., Liu, Y., Wang, Z., Tian, C., & Zhang, Q. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00803-6
Human activity recognition with contrastive learning and TimeGAN-Based data augmentation
Jahangir, R., Nauman, M. A., Alturki, N. M., Ramzan, F., & Yameen, M. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05451-x
Lou, J., Wang, S., Lü, S., Yang, J., Hou, B., & Zhang, R. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3714089
Enhancing human activity recognition through GAN-augmented data and multi-branch CNN-LSTM networks
Shahid, A., Khan, M. H., Nisar, M. A., & Farid, M. S. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116783
Humcareadl: A diverse multimodal dataset and HyMCL-Net for robust human activity recognition
Ashraf, A., Ali, M. H., Ashfaq, N., Khan, M. H., & Farid, M. S. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21905-3
Jegminat, J., Shahnawaz, S., Rodrigues, J., Danieletto, M., Landell, K., Campanella, G. S. V., Garber, C. E., Fayad, Z. A., & Ensari, I. (2026) · npj Women s Health · doi:10.1038/s44294-026-00156-5
- Authors
- Cheng, D., Zhang, L., Bu, C., Wu, H., & Song, A.
- Affiliations
- Nanjing Normal University (CN)
- Yunnan University (CN)
- Southeast University (CN)
- Corresponding author
- Zhang, Lei
- Wu, Hao
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 276
- Pages
- 110789
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4383620541
- Cited by
- 55
- References
- 63
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 28 · 2023: 3
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- 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 › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- human activity recognition, contrastive learning, time series data augmentation, hierarchical feature learning, wearable sensors, deep supervision, invariance learning, semi-supervised learning, multi-level feature fusion
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
