# Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1903.12489 · HTML: https://mlsanity.com/paper/1903.12489/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 30 of 35)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [SWL-Adapt: An Unsupervised Domain Adaptation Model with Sample Weight Learning for Cross-User Wearable Human Activity Recognition](https://mlsanity.com/paper/2212.00724/) (2022)
- [Domain Generalization for Activity Recognition via Adaptive Feature Fusion](https://mlsanity.com/paper/2207.11221/) (2022)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Transfer Learning in Human Activity Recognition: A Survey](https://mlsanity.com/paper/2401.10185/) (2024)
- [Large Language Models are Zero-Shot Recognizers for Activities of Daily Living](https://mlsanity.com/paper/2407.01238/) (2024)
- [TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation](https://mlsanity.com/paper/2209.09092/) (2022)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity](https://mlsanity.com/paper/2306.13735/) (2023)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [On Provably Robust Meta-Bayesian Optimization](https://mlsanity.com/paper/2206.06872/) (2022)
- [RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition](https://mlsanity.com/paper/2512.08984/) (2025)
- [Cross-user activity recognition via temporal relation optimal transport](https://mlsanity.com/paper/2403.15423/) (2024)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques](https://mlsanity.com/paper/2506.07612/) (2025)
- [Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition](https://mlsanity.com/paper/2403.14682/) (2024)
- [Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey](https://mlsanity.com/paper/2304.06489/) (2023)
- [A Deeply Supervised Semantic Segmentation Method Based on GAN](https://mlsanity.com/paper/2310.04081/) (2023)
- [Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition](https://mlsanity.com/paper/2411.02099/) (2024)
- [Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables](https://mlsanity.com/paper/2110.05732/) (2021)
- [CLAD-Net: Continual Activity Recognition in Multi-Sensor Wearable Systems](https://mlsanity.com/paper/2509.23077/) (2025)
- [SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning](https://mlsanity.com/paper/2304.09530/) (2023)
- [Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2306.05058/) (2023)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems](https://mlsanity.com/paper/2407.09104/) (2024)
- [Domain-Adversarial Anatomical Graph Networks for Cross-User Human Activity Recognition](https://mlsanity.com/paper/2505.06301/) (2025)

## Abstract (arXiv)

Application of intelligent systems especially in smart homes and health-related topics has been drawing more attention in the last decades. Training Human Activity Recognition (HAR) models -- as a major module -- requires a fair amount of labeled data. Despite training with large datasets, most of the existing models will face a dramatic performance drop when they are tested against unseen data from new users. Moreover, recording enough data for each new user is unviable due to the limitations and challenges of working with human users. Transfer learning techniques aim to transfer the knowledge which has been learned from the source domain (subject) to the target domain in order to decrease the models' performance loss in the target domain. This paper presents a novel method of adversarial knowledge transfer named SA-GAN stands for Subject Adaptor GAN which utilizes Generative Adversarial Network framework to perform cross-subject transfer learning in the domain of wearable sensor-based Human Activity Recognition. SA-GAN outperformed other state-of-the-art methods in more than 66% of experiments and showed the second best performance in the remaining 25% of experiments. In some cases, it reached up to 90% of the accuracy which can be obtained by supervised training over the same domain data.