Transfer learning for activity recognition: a survey
Open accessAbstract
Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.
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Although TL enhances the accuracy of several tasks in pattern recognition and computer vision, there is an essential issue related to the source data type used by TL as compared to the target dataset.
“Nevertheless, although TL enhances the accuracy of several tasks in the fields of pattern recognition and computer vision [154, 155], there is an essential issue related to the source data type used by the TL as compared to the target dataset.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [154]
Section 4 (Physical Setting Differences; page unavailable) identifies differences between source and target datasets, including sensor type, and says that different sensor modalities make transfer learning much more difficult. The Introduction describes improved performance as a goal of transfer learning, but the copy does not establish the claimed accuracy gains across several pattern-recognition and computer-vision tasks. (LLM, conf=0.83)
Experimental — accuracy not yet measuredWe follow state-of-the-art UDA evaluation methodology by performing stratified split on target domain data into 60% training, 20% validation, and 20% testing.
“For each run, we follow the state-of-the-art UDA evaluation methodology [9,44,54] by performing stratified split on the target domain data into 60% for training, 20% for validation, and 20% for testing.”
From ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning (2021) · cites this paper as [9]
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- Authors
- Cook, D., Feuz, K. D., & Krishnan, N. C.
- Affiliations
- Washington State University (US)
- Corresponding author
- Cook, Diane
- Venue
- Knowledge and Information Systems
- Published
- 2013-06-07
- Volume
- 36
- Issue
- 3
- Pages
- 537-556
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- PMC
- PMC3768027
- OpenAlex
- W2037265949
- Cited by
- 447
- References
- 88
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 36 · 2023: 33 · 2022: 57 · 2021: 56 · 2020: 59 · 2019: 55 · 2018: 42 · 2017: 23 · 2016: 17 · 2015: 6 · 2014: 12 · 2013: 5 · 2012: 1
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Knowledge and Information Systems (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675, R01EB015853)
- 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
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- transfer learning, activity recognition, human activity recognition, sensor modalities, domain adaptation, data availability, information transmission
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