期刊
SENSORS
卷 21, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/s21030885
关键词
human activity recognition (HAR); wearable device; air pressure sensor; inertial measurement unit (IMU); transfer learning
资金
- Innovation Fund of WNLO
- Fundamental Research Funds for the Central Universities [HUST: 2019kfyXKJC019, 2019kfyRCPY014]
- Joint fund of Science & Technology Department of Liaoning Province
- State Key Laboratory of Robotics, China
Human activity recognition based on wearable devices has gained more attention from researchers, with a focus on personalized recognition and high accuracy while maintaining model generalization. A new transfer learning algorithm with improved pseudo-labels was proposed to address personalized recognition challenges and achieved a high average recognition accuracy of 93.2% for different subjects.
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%.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据