A unified generative model using generative adversarial network for activity recognition

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CNN based approach for activity recognition using a wrist-worn accelerometer

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SensoryGANs: An Effective Generative Adversarial Framework for Sensor-based Human Activity Recognition

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A novel approach to generate a large scale of supervised data for short text sentiment analysis

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Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach

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BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation

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Improving Skin Lesion Segmentation with Generative Adversarial Networks

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A novel orientation- and location-independent activity recognition method

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Designing towards the Unknown: Engaging with Material and Aesthetic Uncertainty

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Heartbeat Anomaly Detection using Adversarial Oversampling

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A novel feature selection method based on comparison of correlations for human activity recognition problems

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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

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Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

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State-of-the-Art in 1D Convolutional Neural Networks: A Survey

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A survey on unsupervised learning for wearable sensor-based activity recognition

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Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

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A survey on state-of-the-art deep learning applications and challenges

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Semi-supervised and personalized federated activity recognition based on active learning and label propagation

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A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition

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Federated Clustering and Semi-Supervised learning: A new partnership for personalized Human Activity Recognition

Presotto, R., Civitarese, G., & Bettini, C. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101726 · Cited by 27 · see citing works

HMGAN

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Fully Connected Generative Adversarial Network for Human Activity Recognition

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Conditional Generative Adversarial Network-Based Regression Approach for Walking Distance Estimation Using Waist-Mounted Inertial Sensors

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Efficient human activity recognition: A deep convolutional transformer-based contrastive self-supervised approach using wearable sensors

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Personalized Semi-Supervised Federated Learning for Human Activity RecognitionPreprint

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Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity

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Comparing self-supervised learning techniques for wearable human activity recognition

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Enhanced dataset synthesis using conditional generative adversarial networks

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Multi-Framework Evidential Association Rule Fusion for Wearable Human Activity Recognition

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A Comparative Study of Deep Learning Approaches for Human Action Recognition

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Advances in AI: Employing Deep Generative Models for the Creation of Synthetic Healthcare Datasets to Improve Predictive Analytics

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Experience: A Comparative Analysis of Multivariate Time-Series Generative Models: A Case Study on Human Activity Data

Alzahrani, N., Cała, J., & Paolo Missier, B. (2024) · Journal of Data and Information Quality · doi:10.1145/3688393 · Cited by 2 · see citing works

Multi-Modal Vision and Language Models for Real-Time Emergency Response

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Characterization and Classification of Tactical Movements Using Wearable Motion Sensors and Deep Learning Models

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HAR-STGAN: Human Activity Recognition-Spatial Transformer network for Generative Adversarial Network

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A Generative Adversarial Network Framework for Generating Synthetic Road Condition Data from Accelerometers

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Multi-headed Convolutional Embedded with Recurrent Networks for Recognizing the Cross-Subject Activity Using Transfer Learning

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An Effective GANs-Based Method for High-Information-Value Multimodal Wearable Sensor Data Synthesis

Wang, J., Gu, Y., Zhou, S., & Liu, Z. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227338

ActivityGAN: generative adversarial networks for data augmentation in sensor-based human activity recognition

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SynDa: A Novel Synthetic Data Generation Pipeline for Activity Recognition

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Experience: A Comparative Analysis of Multivariate Time-Series Generative Models: A Case Study on Human Activity Data

Alzahrani, N., Cała, J., & Paolo Missier, B. (2024) · Journal of Data and Information Quality · doi:10.1145/3688393 · Cited by 2 · see citing works

Using Synthetic Data to Improve the Accuracy of Human Activity Recognition

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Real-Time Human Activity Generation using Bidirectional Long Short Term Memory Networks

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