The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Human Activity Recognition Based on Deep Learning Method
Abstract
With the increasing demand of security defense., anti-terrorism investigation and disaster rescue., human activity classification and recognition have become a hot research topic. When a human is illuminated by electromagnetic waves., a Doppler signal is generated from his or her moving parts. Indeed., bodily movements are what make humans micro-Doppler signatures unique., offering a chance to classify human activities. Classification needs a lot of samples for training., however., in the real application., there is a certain gap between the simulated data and the real data., and the measured data is often difficult to obtain. Due to the nonstationary characteristic for human radar echoes., the spectrograms for the human activities show different micro-Doppler signatures. Therefore., we proposed a method of human activity classification based on spectrograms using deep learning techniques., including deep convolutional generative adversarial network for expanding and enriching training set and a transfer-learned deep convolutional network (DCNN) for feature extraction and classification., which is based on a DCNN pre-trained by a large-scale RGB image data set-that is., ImageNet. Finally., the simulation results verified the effectiveness of the proposed method.
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“In [241,242], they generated verisimilar data of different activities, and Shi et al. [243] used the Boulic kinematic model, which aims to capture the three-dimensional positioning trend to synthesize personified walking data.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [243]
Citations
Cites
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
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
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Sorted by citations
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
“[56, 170] generate verisimilar data of different activities and [248] uses the Boulic kinematic model which aims to capture the three dimensional positioning trend to synthesize personified walking data.”
MethodSelf-citationContinuous 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
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
Metadata
- Authors
- Shi, X., Li, Y., Zhou, F., & Liu, L.
- Affiliations
- Xidian University (CN)
- Venue
- 2018 International Conference on Radar (RADAR)
- Publisher
- IEEE
- Published
- 2018-08
- Pages
- 1-5
- OpenAlex
- W2904273371
- Cited by
- 45
- References
- 13
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 10 · 2022: 10 · 2021: 5 · 2020: 4 · 2019: 4
- FWCI
- 5.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Radar (RADAR) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- human activity recognition, micro-Doppler signatures, DCGAN, transfer learning, non-stationary signals
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