Human activity recognition using wearable sensors by heterogeneous convolutional neural networks

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Squeeze-and-Excitation Networks

Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853 · see citing works

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works

Nonlinear total variation based noise removal algorithms

Rudin, L., Osher, S., & Fatemi, E. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 17,089 · see citing works

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · see citing works

A Convolutional Neural Network for Modelling Sentences

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · 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

A Survey on Human Activity Recognition using Wearable Sensors

Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · 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

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

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

Ignatov, A. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.027 · Cited by 817 · see citing works

2018 International Joint Conference on Neural Networks (IJCNN)

(2018) · doi:10.1109/ijcnn40505.2018 · Cited by 770 · see citing works

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. D. R., & Roggen, D. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.014 · Cited by 742 · see citing works

Sensor-based and vision-based human activity recognition: A comprehensive survey

Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730 · see citing works

Advanced Computing in Electron Microscopy

Kirkland, E. J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6533-2 · Cited by 707 · see citing works

Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks

Jiang, W., & Yin, Z. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2806333 · Cited by 676 · see citing works

Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables

Hammerla, N. Y., Halloran, S., & Ploetz, T. (2016) · arXiv · doi:10.48550/arxiv.1604.08880 · Cited by 606 · see citing works

Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

Zhou, X., Liang, W., Wang, K. I., Wang, H. H., Yang, L. T., & Jin, Q. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2985082 · Cited by 557 · see citing works

USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors

Zhang, M., & Sawchuk, A. A. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370438 · Cited by 554 · see citing works

Improving Convolutional Networks With Self-Calibrated Convolutions

Liu, J., Hou, Q., Cheng, M., Wang, C., & Feng, J. (2020) · 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/cvpr42600.2020.01011 · Cited by 529 · see citing works

A survey on wearable sensor modality centred human activity recognition in health care

Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409 · see citing works

A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer

Chen, Y., & Xue, Y. (2015) · 2015 IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.263 · Cited by 378 · see citing works

2015 IEEE International Conference on Systems, Man, and Cybernetics

(2015) · doi:10.1109/smc35812.2015 · Cited by 309 · see citing works

Learning Pooling for Convolutional Neural Network

Sun, M., Song, Z., Jiang, X., Pan, J. S., & Pang, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.049 · Cited by 277 · see citing works

2017 International Conference on Engineering and Technology (ICET)

(2017) · doi:10.1109/icet42298.2017 · Cited by 272 · see citing works

A federated learning system with enhanced feature extraction for human activity recognition

Xiao, Z., Xu, X., Xing, H., Song, F., Wang, X., & Zhao, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107338 · Cited by 251 · see citing works

Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities’ Recognition With Wearable Sensors

Wang, K., He, J., & Zhang, L. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2917225 · Cited by 205 · 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

Understanding and improving recurrent networks for human activity recognition by continuous attention

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

Down-scaling for better transform compression

Bruckstein, A. M., Elad, M., & Kimmel, R. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.816023 · Cited by 176 · see citing works

Smart Home: Cognitive Interactive People-Centric Internet of Things

Feng, S., Setoodeh, P., & Haykin, S. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600682cm · Cited by 157 · see citing works

Bilinear interpolation theorems and applications

Mastyło, M. (2013) · Journal of Functional Analysis · doi:10.1016/j.jfa.2013.05.001 · Cited by 154 · 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

Masked reconstruction based self-supervision for human activity recognition

Haresamudram, H., Beedu, A., Agrawal, V., Grady, P. L., Essa, I., Hoffman, J., & Plötz, T. (2020) · Proceedings of the 2020 International Symposium on Wearable Computers · doi:10.1145/3410531.3414306 · Cited by 146 · see citing works

2017 IEEE International Conference on Big Data and Smart Computing (BigComp)

(2017) · doi:10.1109/bigcomp39579.2017 · Cited by 140 · see citing works

Meta-HAR: Federated Representation Learning for Human Activity Recognition

Li, C., Niu, D., Jiang, B., Zuo, X., & Yang, J. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3450006 · Cited by 133 · see citing works

Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors

Huang, W., Zhang, L., Gao, W., Min, F., & He, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3091990 · Cited by 129 · see citing works

A novel random forests based class incremental learning method for activity recognition

Hu, C., Chen, Y., Hu, L., & Peng, X. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.025 · Cited by 121 · see citing works

HASC Challenge

Kawaguchi, N., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Inoue, S., Kawahara, Y., Sumi, Y., & Nishio, N. (2011) · Augmented Human International Conference · doi:10.1145/1959826.1959853 · Cited by 113 · see citing works

Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution

Gao, W., Zhang, L., Huang, W., Min, F., He, J., & Song, A. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3102735 · Cited by 107 · see citing works

Human Activity Recognition for Healthcare using Smartphones

Ogbuabor, G. O., & La, R. (2018) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3195106.3195157 · Cited by 100 · see citing works

2012 16th International Symposium on Wearable Computers

(2012) · doi:10.1109/iswc20335.2012 · Cited by 90 · 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

Human Activity Recognition Based on Dynamic Active Learning

Bi, H., Perello-Nieto, M., Santos-Rodriguez, R., & Flach, P. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3013403 · Cited by 81 · see citing works

Binarized Neural Network for Edge Intelligence of Sensor-Based Human Activity Recognition

Luo, F., Khan, S., Huang, Y., & Wu, K.-S. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3109940 · Cited by 61 · see citing works

Sequential Weakly Labeled Multiactivity Localization and Recognition on Wearable Sensors Using Recurrent Attention Networks

Wang, K., He, J., & Zhang, L. (2021) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3086008 · Cited by 53 · see citing works

Evidential 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

Learning Disentangled Representation for Mixed- Reality Human Activity Recognition With a Single IMU Sensor

Xia, S., Chu, L., Pei, L., Zhang, Z., Yu, W., & Qiu, R. C. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3111996 · Cited by 40 · see citing works

Adding structural characteristics to distribution-based accelerometer representations for activity recognition using wearables

Kwon, H., Abowd, G. D., & Plötz, T. (2018) · ACM International Symposium on Wearable Computers (ISWC) · doi:10.1145/3267242.3267258 · Cited by 37 · see citing works

Proceedings. DELTA 2004. Second IEEE International Workshop on Electronic Design, Test and Applications

(2004) · IEEE International Workshop on Electronic Design, Test and Applications (DELTA) · doi:10.1109/delta.2004.2 · Cited by 14 · see citing works

Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach

Guo, P., & Nakayama, M. (2025) · Sensors · doi:10.3390/s25226988 · Cited by 6 · see citing works

Framework for Activity Recognition Using Smart Devices

Aly, W. M., Eskaf, K., & Aly, A. (2015) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta37466.2015.9513456

Can Activities of Human Daily Life be Recognized and Predicted?

Gu, Y., Liu, W., & Song, J. (2015) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2015.61 · Cited by 1 · see citing works

An Adaptable Real-Time Object Detection for Traffic Surveillance using R-CNN over CNN with Improved Accuracy

Vinod, G., & Padmapriya, G. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759030 · Cited by 11 · see citing works

Human Activity Recognition Based on Similarity

Zhu, Y., Wang, C., Zhang, J., et al. (2014) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2014.262 · Cited by 10 · see citing works

Human Activity Recognition Using Place-Based Decision Fusion in Smart Homes

Cumin, J., Lefebvre, G., Ramparany, F., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57837-8_11 · Cited by 17 · see citing works