# Transfer Learning in Human Activity Recognition: A Survey

arXiv: https://arxiv.org/abs/2401.10185 · HTML: https://mlsanity.com/paper/2401.10185/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 201 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 30 of 34)

- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Multi-Modal Domain Adaptation for Fine-Grained Action Recognition](https://mlsanity.com/paper/2001.09691/) (2020)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data](https://mlsanity.com/paper/2005.10996/) (2020)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Going Deeper into Action Recognition: A Survey](https://mlsanity.com/paper/1605.04988/) (2016)
- [Stratified Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1801.00820/) (2017)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)
- [Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition](https://mlsanity.com/paper/2206.06629/) (2022)
- [Deep Transfer Learning for Cross-domain Activity Recognition](https://mlsanity.com/paper/1807.07963/) (2018)
- [Cross-domain Activity Recognition via Substructural Optimal Transport](https://mlsanity.com/paper/2102.03353/) (2021)
- [A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning](https://mlsanity.com/paper/2111.04418/) (2021)
- [Domain Generalization for Activity Recognition via Adaptive Feature Fusion](https://mlsanity.com/paper/2207.11221/) (2022)
- [Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks](https://mlsanity.com/paper/2012.04479/) (2020)
- [Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization](https://mlsanity.com/paper/2005.12178/) (2020)
- [Cross-position Activity Recognition with Stratified Transfer Learning](https://mlsanity.com/paper/1806.09776/) (2018)
- [A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors](https://mlsanity.com/paper/2003.01874/) (2020)
- [A survey on VQA_Datasets and Approaches](https://mlsanity.com/paper/2105.00421/) (2021)
- [Personalized Human Activity Recognition Using Convolutional Neural Networks](https://mlsanity.com/paper/1801.08252/) (2018)
- [TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation](https://mlsanity.com/paper/2209.09092/) (2022)
- [MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model](https://mlsanity.com/paper/2009.09404/) (2020)
- [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)
- [Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring](https://mlsanity.com/paper/2012.02539/) (2020)
- [Appropriateness of Performance Indices for Imbalanced Data Classification: An Analysis](https://mlsanity.com/paper/2008.11752/) (2020)
- [ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition](https://mlsanity.com/paper/2003.07415/) (2020)

## Built on by (most cited first, 14 of 14)

- [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)
- [Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)](https://mlsanity.com/paper/2405.12368/) (2024)
- [Large Language Models are Zero-Shot Recognizers for Activities of Daily Living](https://mlsanity.com/paper/2407.01238/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques](https://mlsanity.com/paper/2506.07612/) (2025)
- [HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets](https://mlsanity.com/paper/2503.13542/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)
- [Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images](https://mlsanity.com/paper/2501.18453/) (2025)
- [XTransfer: Modality-Agnostic Few-Shot Model Transfer for Human Sensing at the Edge](https://mlsanity.com/paper/2506.22726/) (2025)
- [Multilevel neural networks with dual-stage feature fusion for human activity recognition](https://mlsanity.com/paper/2604.16577/) (2026)
- [Scene-Adaptive Continual Learning for CSI-based Human Activity Recognition with Mixture of Experts](https://mlsanity.com/paper/2605.06447/) (2026)

## Abstract (arXiv)

Sensor-based human activity recognition (HAR) has been an active research area, owing to its applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has resulted in state-of-the-art performance in domains such as computer vision and natural language, where large amounts of annotated data are available. However, large quantities of annotated data are not available for sensor-based HAR. Moreover, the real-world settings on which the HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been employed extensively. In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR. In particular, we provide a problem-solution perspective by categorizing and presenting the works in terms of their contributions and the challenges they address. We also present an updated view of the state-of-the-art for both application domains. Based on our analysis of 205 papers, we highlight the gaps in the literature and provide a roadmap for addressing them. This survey provides a reference to the HAR community, by summarizing the existing works and providing a promising research agenda.