SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition
Abstract
This study focuses on improving the performance of human activity recognition when a small number of sensor data are available under some special practical scenarios and resource-limited environments, such as some high-risk projects, anomaly monitoring and actual tactical scenarios. The Human Activity Recognition (HAR) based on wearable sensors is an attractive research topic in machine learning and ubiquitous computing over the last few decades, and has extremely practicality in health surveillance, medical assistance, personalized services, etc. However, with the limitation of sensor sampling rate, sustainability, deployment, and other restricted conditions, it is difficult to collect enough and resultful sensor data anywhere, anytime. Therefore, the HAR based on wearable sensors always faces the challenges of the low-data regime under some practical scenarios, which leads to a low accuracy of activity recognition and needs to be solved urgently. Currently, the Generative Adversarial Networks (GANs) provide a powerful method for training resultful generative models that could generate very convincing verisimilar images. The framework of GANs and its variants shed many lights on improving the performance of HAR. In this paper, we propose a new generative adversarial networks framework called SensoryGANs that can effectively generate available sensor data used for HAR. To the best of our knowledge, SensoryGANs is the first unbroken generative adversarial networks applied in generating sensor data in the HAR research field. Firstly, we tried exploring and devising three activity-special GANs models for three human daily activities. Secondly, these specific models are trained with the guidance of unbroken vanilla GANs. Thirdly, the trained generators from adversarial optimization process are used to generate synthetic sensor data. Finally, the synthetic sensor data from SensoryGANs are used to enrich the original authentic sensor datasets, which can improve the performance of target activity recognition model. Meanwhile, we propose three visual evaluation methods for assessing synthetic sensor data produced by the trained generators in SensoryGANs models. Experimental results show that SensoryGANs models have the capability of capturing the implicit distribution of real sensor data of human activity, and then the synthetic sensor data generated by SensoryGANs models Have a potential for improving human activity recognition.
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Wang et al. utilized GANs with a customized network to generate synthetic data from HASC2010corpus.
“Wang et al. [238] utilized GANs with a customized network to generate synthetic data from the public HAR dataset HASC2010corpus [239].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [238]
Deep learning methods are used to augment datasets to improve performance.
“Deep learning methods are also used to augment the datasets to improve performance [238,263,264].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [238]
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Improving Smartphone-Based Transport Mode Recognition Using Generative Adversarial Networks
Günthermann, L., Philippides, A. O., & Roggen, D. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-8944-7_5 · Cited by 3 · see citing works
A Workflow for Synthetic Biometric in-Edge Data Generation using GAN based models
do Rêgo, L. G. C., da Silva, T. L. C., Rocha, A. R. D., Monteiro, M. S., Cruz, L. A., Zeitouni, K., Soares, J. B., & de Macêdo, J. A. F. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09938-9 · Cited by 2 · see citing works
Alzahrani, N., Cała, J., & Paolo Missier, B. (2024) · Journal of Data and Information Quality · doi:10.1145/3688393 · Cited by 2 · see citing works
A Comprehensive Review of Deep Learning for Activity Recognition
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Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition
Luo, J., Li, X., & Younes, R. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-0575-8_3 · Cited by 2 · see citing works
CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity Recognition
Zhang, X., Shan, C., Zhu, T., Chen, L., Zhou, F., & Ning, H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3238064 · Cited by 2 · see citing works
Virtual Sensors With 3D Digital Human Motion for Interactive Simulation
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Pervasive Lying Posture Tracking
Alinia, P., Samadani, A., Milošević, M., Ghasemzadeh, H. G., & Parvaneh, S. (2020) · Sensors · doi:10.3390/s20205953 · Cited by 2 · see citing works
Guided-GAN: Adversarial Representation Learning for Activity Recognition with WearablesPreprint
Abedin, A., Rezatofighi, H., & Ranasinghe, D. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05732 · Cited by 5 · see citing works
Wi-Limb: Recognizing Moving Body Limbs Using a Single WiFi Link
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Trust-Centric Mitigation of Time-Warping Adversarial Attacks in Wearable Sensor Platforms
Sahay, R., Lakshmi, L., Sai, P. V. J., Althobaiti, M. M., Salhi, A., Khan, M. I., & Ismoilov, M. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3681806 · Cited by 1 · see citing works
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Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition
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Self-citationEnhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis
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Self-citationA rehabilitation activity monitoring method based on Shallow-CNN
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Contributions to Human Motion Modeling and Recognition using Non-intrusive Wearable Sensors
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“By capturing the statistical properties of sensor data, the model generates synthetic sensor data closely resembling the characteristics of the original data.”
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Efficient Human Activity Recognition Based on Grouped Representations of Multimodal Wearable Data
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Real-Time Human Activity Recognition for Elderly VR Training with Body Area Networks
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Generative AI Models for Sensor Data Processing
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An Effective GANs-Based Method for High-Information-Value Multimodal Wearable Sensor Data Synthesis
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Self-citationGAN-HLT: Generative Hierarchical Light-Transformer for Extendable Human Activity Recognition
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Multimodal Situation-aware Data Generation for Activity Recognition in Intelligent Wearable Systems
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- Authors
- Wang, J., Chen, Y., Gu, Y., Xiao, Y., & Pan, H.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2018 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-8
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2897551993
- Cited by
- 92
- References
- 34
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 13 · 2023: 16 · 2022: 21 · 2021: 12 · 2020: 11 · 2019: 2
- FWCI
- 1.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- 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 › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- human activity recognition, wearable sensors, generative adversarial networks, low-data regimes, data augmentation, resource-constrained environments, daily activities
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