Transfer Learning in Sensor-Based Human Activity Recognition: A Survey
Abstract
Sensor-based human activity recognition (HAR) has been an active research area for many years, resulting in practical applications in smart environments, assisted living, fitness, healthcare, and more. Recently, deep-learning-based end-to-end training has pushed the 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 typically not available for sensor-based HAR. Moreover, the real-world settings on which HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been explored 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 present an overview of the state of the art for both application domains. Based on our analysis of 246 papers, we highlight the gaps in the literature and provide a roadmap for addressing these. This survey provides a reference to the HAR community by summarizing the existing works and providing a promising research agenda.
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Video and IMU signals are heterogeneous with different temporal resolutions and feature spaces.
“Aligning heterogeneous video and IMU signals with different temporal resolutions and feature spaces [12].”
From COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition (2026) · cites this paper as [12]
Cites
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COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Chen, B., Wongso, W., Li, Z., Khaokaew, Y., Xue, H., & Salim, F. D. (2026) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3810218 · Cited by 1 · see citing works
Temporal Distance Based Self-Supervised Learning for Human Activity Recognition
Bigelli, L., Contoli, C., Kania, N., & Lattanzi, E. (2025) · 2025 Sixth International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta66210.2025.11202839
- Authors
- Dhekane, S. G., & Ploetz, T.
- Affiliations
- Georgia Institute of Technology (US)
- Georgia Institute of Technology, Atlanta, United States
- Venue
- ACM Computing Surveys
- Published
- 2025-03-23
- Volume
- 57
- Issue
- 8
- Pages
- 1-39
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4407543209
- Cited by
- 42
- References
- 21
- Citations by year
- 2026: 27 · 2025: 15
- FWCI
- 14.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Funders
- CISCO
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- transfer learning, human activity recognition, wearable sensors, home automation, sensor modalities, limited labeled data, assisted living, smart environments, deep learning
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