Human Activity Recognition Based on Deep Learning Method

Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

Kim, Y., & Ling, H. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2012849 · Cited by 887

Generative adversarial networks: introduction and outlook

Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510583 · Cited by 844

The Micro-Doppler Effect in Radar

Chen, V. C. (2010) · Cited by 717

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Kim, Y., & Moon, T. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2491329 · Cited by 662

Time-Frequency Signal Analysis With Applications

Stanković, L., Daković, M., & Thayaparan, T. (2013) · Cited by 219

Synthetic Aperture Radar Image Synthesis by Using Generative Adversarial Nets

Guo, J., Lei, B., Ding, C., & Zhang, Y. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2699196 · Cited by 174

Simulation and Analysis of Human Micro-Dopplers in Through-Wall Environments

Ram, S. S., Christianson, C., Kim, Y., & Ling, H. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2037219 · Cited by 170

Classification of human motions using empirical mode decomposition of human micro‐Doppler signatures

Fairchild, D. P., & Narayanan, R. M. (2014) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2013.0165 · Cited by 160

Infrared Image Colorization Based on a Triplet DCGAN Architecture

Suárez, P. L., Sappa, A. D., & Vintimilla, B. X. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.32 · Cited by 158

Human gait recognition with cane assistive device using quadratic time–frequency distributions

Amin, M. G., Ahmad, F., Zhang, Y. D., & Boashash, B. (2015) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2015.0119 · Cited by 37

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

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

Shrestha, A., Li, H., Kernec, J. L., & Fioranelli, F. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3006386 · Cited by 243

Micro-Doppler Based Target Recognition With Radars: A Review

Hanif, A., Muaz, M., Hasan, A., & Adeel, M. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3141213 · Cited by 201

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Erol, B., Gürbüz, S. Z., & Amin, M. G. (2020) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2020.2969579 · Cited by 112

Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures

Zhu, S., Guendel, R. G., Yarovoy, A. G., & Fioranelli, F. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3189746 · Cited by 102

Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

Ahmed, S., & Cho, S. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3334269 · Cited by 98

An improved human activity recognition technique based on convolutional neural network

Ravi Raj, K. G., & Kos, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-49739-1 · Cited by 97

Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity

Alnujaim, I., Oh, D., & Kim, Y. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2919770 · Cited by 79

Human Motion Recognition With Limited Radar Micro-Doppler Signatures

Li, X., He, Y., Fioranelli, F., Jing, X., Yarovoy, A. G., & Yang, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3028223 · Cited by 54

Human Motion Recognition With Spatial-Temporal-ConvLSTM Network Using Dynamic Range-Doppler Frames Based on Portable FMCW Radar

Ding, C., Zhang, L., Chen, H., Hong, H., Zhu, X., & Li, C. (2022) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2022.3200097 · Cited by 49

Device Position-Independent Human Activity Recognition with Wearable Sensors Using Deep Neural Networks

Mekruksavanich, S., & Jitpattanakul, A. (2024) · Applied Sciences · doi:10.3390/app14052107 · Cited by 41

Human Activity Recognition Based on WRGAN-GP-Synthesized Micro-Doppler Spectrograms

Qu, L., Wang, Y., Tian-hong, Y., & Sun, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3164152 · Cited by 38

A Novel Deep Transfer Learning Approach Based on Depth-Wise Separable CNN for Human Posture Detection

Ogundokun, R. O., Maskeliūnas, R., Misra, S., & Damaševičius, R. (2022) · Information · doi:10.3390/info13110520 · Cited by 31

Generative Adversarial Networks to Augment Micro-Doppler Signatures for the Classification of Human Activity

Alnujaim, I., Oh, D., & Kim, Y. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898073 · Cited by 25

Fusion of Deep Representations in Multistatic Radar Networks to Counteract the Presence of Synthetic Jamming

Patel, J. S., Fioranelli, F., Ritchie, M., & Griffiths, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2909685 · Cited by 21

Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition

Yang, S., Baek, D.-G., & Thapa, K. (2022) · Sensors · doi:10.3390/s22134755 · Cited by 18

Lightweight Multiattention Enhanced Fusion Network for Omnidirectional Human Activity Recognition With FMCW Radar

Li, X., Qiu, Y., Deng, Z., Liu, X., & Huang, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3489956 · Cited by 16

Human Behavior Analysis Using Radar Data: A Survey

Miazek, P., Żmudzińska, A., Karczmarek, P., & Kiersztyn, A. (2024) · IEEE Access · doi:10.1109/access.2024.3474100 · Cited by 15

Deep Analysis for Smartphone-based Human Activity Recognition

Shan, C. Y., Han, P. Y., & Yin, O. S. (2020) · doi:10.1109/icoict49345.2020.9166229 · Cited by 15

Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

Thapa, K., Seo, Y., Yang, S., & Kim, K. (2023) · Sensors · doi:10.3390/s23020683 · Cited by 12

Multiple Human Activities Classification Based on Dynamic On-Body Propagation Characteristics Using Transfer Learning

Zhang, Y., Shao, Y., Luo, R., Xiong, L. Y., & Zhang, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3321056 · Cited by 12

Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition

Islam, S. M. M., & Talukder, K. H. (2023) · IEEE Access · doi:10.1109/access.2023.3314651 · Cited by 11

Radar-Based Continuous Human Activity Recognition Using Multidomain Fusion Vision Transformer

Qu, L., Li, X., Tian-hong, Y., & Wang, S. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3535673 · Cited by 9

Human Activity Detection Using Pose Net

Duth, P. S., & Poojashree, B. S. (2022) · International Conference on Futuristic Technologies (INCOFT) · doi:10.1109/incoft55651.2022.10094346 · Cited by 9

machine and deep learning approaches for human activity recognition

Alhumayyani, M. M., Mounir, M., & Ismail, R. M. (2021) · International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences · doi:10.21608/ijicis.2021.82008.1106 · Cited by 8

A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors

Mekruksavanich, S., Tancharoen, D., & Jitpattanakul, A. (2023) · doi:10.1109/incit60207.2023.10413113 · Cited by 8

Through-Wall Human Activity Recognition With Dual-Layer Attention Augmented Multiscale Multiview Feature Fusion Network Using Low-Frequency Multistatic Bio-Radar

Zhao, Y., Yong Qiang, J. I. A., Huang, D. J., Zhang, L., Zheng, Y., Wang, J., & Qi, F. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3565041 · Cited by 7

A Neural Network Pruning Approach to Human Activity Identification Based on Deep Learning

Mahor, V. K., & Pandit, J. (2022) · IEEE International Conference on Current Development in Engineering and Technology (CCET) · doi:10.1109/ccet56606.2022.10079945 · Cited by 7

Radar Dataset Synthesis Approach for Gesture Recognition

Zhao, Y., Sark, V., Krstic, M. D., & Grass, E. (2023) · European Radar Conference (EuRAD) · doi:10.23919/eurad58043.2023.10289560 · Cited by 6

Anomaly detection on in-home activities data based on time interval

Poh, S.-C., Tan, Y.-F., Cheong, S.-N., Ooi, C., & Tan, W.-H. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v15.i2.pp778-785 · Cited by 6

WGAN-GP-Based Synthetic Radar Spectrogram Augmentation in Human Activity Recognition

Qu, L., Wang, Y., Tian-hong, Y., Zhang, L., & Sun, Y. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554556 · Cited by 6

Generative Adversarial Network for Radar-Based Human Activities Classification with Low Training Data Support

Wu, C., & Ye, W. (2021) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict53123.2021.9531147 · Cited by 5

A Lightweight DCNN Algorithm for Radar-Based Suspicious Human Activities Classification With Data Augmentation Techniques

Waghumbare, A. A., Singh, U., Raj, A. A. B., & Singhal, N. (2025) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2025.3581164 · Cited by 3

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Korti, D. S., & Slimane, Z. (2023) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.14.8.7 · Cited by 3

Activity Identification from Natural Images Using Deep CNN

Hossain, M. A., & Hasan, M. A. F. M. R. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-68154-8_60 · Cited by 2

Industrial Human Activity Prediction and Detection Using Sequential Memory Networks

Tuli, T. B., Patel, V. M., & Manns, M. (2022) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/12144 · Cited by 2

Human Activity Classification Using Deep Convolutional Neural Network

Verma, A., Suman, A., Biradar, V. G., & Brunda, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3342-3_4 · Cited by 2

Few-shot generative adversarial network for radar micro-doppler spectrogram synthesis with application to human activity recognition

Wei, F., Song, T., Huang, X., & Gao, C. (2024) · International Conference on Digital Image Processing · doi:10.1117/12.3038540 · Cited by 1

Multiple Parallel Activity Detection and Recognition to Avoid COVID-19 Spread-Out

Ubaid, M. T., Khan, M. U. G., Khan, M. U. G., Rehman, A. U., & Ayesha, N. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7618-5_18 · Cited by 1

Action Classification in Human Robot Interaction Cells in Manufacturing

Mehak, S., Leva, M. C., Kelleher, J. D., & Guilfoyle, M. (2023) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3589883.3589916 · Cited by 1

Human Activity Recognition using Low Cost Doppler Radar Sensor Network and CNN

Deepthy, G. S., Peter, K., Mathew, E. T., Bijoy, M., & Jojo, M. (2023) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc59169.2023.10334979 · Cited by 1

Elderly monitoring using decision trees under domain shifts and computational resource constraints

Atiq, M. (2022) · theses.fr (ABES) · doi:10.70675/3b16f51fz13f3z4752z96afz97bf21e0e234

Human Action Recognition Based on YOLO

Sultanova, А. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94788-9_27

Lightweight Deep Learning for Activity Recognition in Dark Environments

Alkhayyat, A., Sudhamsu, G., Oweis, R. J., Chohan, M. K., Aggarwal, A., Sahu, G., Hota, S., & Gehlot, A. (2026) · National Academy Science Letters · doi:10.1007/s40009-026-01955-9

What Physics do Data-Driven MoCap-to-Radar Models Learn?

Chen, K., Parker, K. W., & Arora, A. (2026) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf2663773.2026.11674916

Transfer and Reinforcement Learning as Support Paradigms for Human Activity Recognition in Indoor Environments: A Comprehensive Analysis of Trends, Impact and Future Directions

Ariza-Colpas, P., Piñeres-Melo, M. A., Carrascal, A. I. O., & Díaz-Jiménez, D. (2026) · Sensors · doi:10.3390/s26123751

TriPath-WGAN-GP for radar-based human activity recognition via micro-Doppler spectrogram augmentation

Waghumbare, A. A., Singh, U., & Raj, A. A. B. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21934-y

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

Kim, Y., & Moon, T. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2491329 · Cited by 662

Deep Learning based Human Activity Classification in Radar Micro-Doppler Image

He, Y., Yang, Y., Lang, Y., Huang, D., Jing, X., & Hou, C. (2018) · European Radar Conference (EuRAD) · doi:10.23919/eurad.2018.8546615 · Cited by 27

Classification in C-band of doppler signatures of human activities in indoor environments

Cadart, P., Merlin, M., Manfredi, G., Fix, J., Ren, C., Hinostroza, I., & Letertre, T. (2021) · IET conference proceedings. · doi:10.1049/icp.2021.0597 · Cited by 4

Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

Kim, Y., & Ling, H. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2009.2012849 · Cited by 887

Transfer Learning for Human Activities Classification Using Micro-Doppler Spectrograms

Du, H., He, Y., & Jin, T. (2018) · IEEE International Conference on Computational Electromagnetics · doi:10.1109/compem.2018.8496654 · Cited by 68