The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A GAN-based data augmentation method for human activity recognition via the caching ability
Abstract
Sensor‐based human activity recognition (HAR) provides vital information for an ocean of health care and entertainment applications. However, data acquisition of such studies usually requires huge time and manpower, which delays the progress of the whole project. Data augmentation is a promising way to solve this issue. In this paper, we expand the origin dataset via a GAN‐based augmentation neural network. The experimental results suggest that the generated data has a certain substitution and complementary effect in terms of the real data based on the caching deployment ability. In addition, the quasi‐real data also gains good performance on identifying certain activities.
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“In the field of HAR, GAN has been applied as a semi-supervised learning approach to deal with unlabeled or partially labelled data for improving performance by learning representations from the unlabeled data, which later will be utilized by the network to generalize to the unseen data distribution [237].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [237]
Citations
Cites
Comparative study on classifying human activities with miniature inertial and magnetic sensors
Altun, K., Barshan, B., & Tunçel, O. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.04.019 · Cited by 601
GAN Augmentation: Augmenting Training Data using Generative Adversarial NetworksPreprint
Bowles, C. T., Chen, L., Guerrero, R., Bentley, P., Gunn, R. N., Hammers, A., Dickie, D. A., Hernández, M. D. C. V., Wardlaw, J. M., & Rueckert, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10863 · Cited by 347
Multi-CDN: Towards Privacy in Content Delivery Networks
Cui, S., Asghar, M. R., & Russello, G. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2018.2833110 · Cited by 32
Research works printed but not linked: 5
- Akshay Uttama Nambi SN, 2017, CoachMe: Activity Recognition using Wearable Devices for Human Augmentation, International Conference on Embedded Wireless Systems and Networks, 174
- Yu T, 2018, CR‐GAN: Learning Complete Representations for Multi‐view Generation, 27th International Joint Conference on Artificial Intelligence, 942
- Cui Zongyong, 2019, Image Data Augmentation for SAR Sensor Via Generative Adversarial Nets, IEEE Access PP.99
- Wang J, 2018, SensoryGANs: An Effective Generative Adversarial Framework for Sensor‐based Human Activity Recognition, 2018 International Joint Conference on Neural Networks (IJCNN), 1
- Nguyen TT, 2016, Mobile Online Activity Recognition System Based on Smartphone Sensors, International Conference on Advances in Information and Communication Dent Tech, 357
Cited by
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
“In the field of HAR, GAN has been applied as a semi-supervised learning approach to deal with unlabeled or partially labeled data for improving performance by learning representations from the unlabeled data which later will be utilized by the network to generalize to the unseen data distribution [178, 205, 260] [247]…”
Method
Metadata
- Authors
- Shi, J., Zuo, D., & Zhang, Z.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Zuo, Decheng
- Venue
- Internet Technology Letters
- Publisher
- Wiley
- Published
- 2020-11-27
- Volume
- 4
- Issue
- 5
- ISSN
- 2476-1508 (ISSN-L)
- OpenAlex
- W3097063581
- Cited by
- 24
- References
- 3
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 5 · 2023: 2 · 2022: 5 · 2021: 3
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Internet Technology Letters (publishedVersion)
- Funders
- National Natural Science Foundation of China (61370085)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human activity recognition, data augmentation, generative adversarial networks, sensor-based activity recognition, synthetic data generation, wearable sensors
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- Shi, J., Zuo, D., & Zhang, Z. (2021). A GAN-based data augmentation method for human activity recognition via the caching ability. Internet Technology Letters. https://doi.org/10.1002/itl2.257
