Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning
Open accessAbstract
Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.
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Personalized HAR methods suffer from increased deployment costs because collecting data from each new user is required.
“While effective, these methods suffer from two major limitations: (1) collecting data from each new user increases deployment costs, and (2) the two-stage training pipeline adds complexity and computational overhead.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [15]
Personalized HAR methods suffer from added complexity and computational overhead due to the two-stage training pipeline.
“While effective, these methods suffer from two major limitations: (1) collecting data from each new user increases deployment costs, and (2) the two-stage training pipeline adds complexity and computational overhead.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [15]
Personalized HAR frameworks typically adopt a two-stage approach: pre-training on a general dataset followed by fine-tuning on data from the target user.
“Prior works have explored personalized HAR frameworks [11,12,13,14,15,16], typically adopting a two-stage approach: pre-training on a general dataset followed by fine-tuning on data from the target user.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [15]
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- Authors
- Fu, Z., He, X., Wang, E., Huo, J., Huang, J., & Wu, D.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Huang, Jian
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-01-28
- Volume
- 21
- Issue
- 3
- Pages
- 885
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7865943
- OpenAlex
- W3125436955
- Cited by
- 79
- References
- 62
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 10 · 2023: 14 · 2022: 19 · 2021: 12
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundamental Research Funds for the Central Universities (HUST: 2019kfyXKJC019, 2019kfyRCPY014)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- human activity recognition, wearable sensors, transfer learning, pseudo-labeling, air pressure sensor, inertial measurement unit, multimodal sensor fusion, wireless sensor nodes, domain adaptation
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Female, Humans, Human Activities, Male
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