Yuan Zong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yuan Zong has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fei Yin
Institute of Automation, Chinese Academy of Sciences
1 shared publication Jinquan Wang
Changchun University of Science and Technology
1 shared publication- Shijie Lv
Jilin University
1 shared publication - Yijun Wang
Changchun University of Science and Technology
1 shared publication - Zhisen Tian
Jilin University
1 shared publication
Latest Publications
- Open Access
- 26 September 2024
- Published by Springer Nature in Research Square
Research on Real-time Surgical Navigation System Based on Machine Learning and Fusion of Multimodal Sensors
In response to the significant challenge of occlusion in optical navigation, this paper proposes an innovative surgical navigation instrument positioning technique based on the fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data. By installing UWB positioning modules and IMU sensors on the surgical instrument arm, this study introduces a machine learning-based data fusion method to fully utilize UWB ranging and IMU high-precision roll and pitch angles, addressing the issues of attitude angles and Z-axis accuracy in UWB pose estimation. The method employs machine learning techniques to model the complex nonlinear relationships among three sets of UWB ranging values, two IMU attitude angles, and the position and yaw angle of the surgical instrument. Experimental results demonstrate that the proposed method achieves an average absolute error of less than 2cm in three-axis position and less than 1° in three-axis attitude angles. This surgical navigation instrument positioning technique exhibits significant advantages in realizing an efficient and accurate surgical navigation system. By optimizing the positioning accuracy of surgical instruments, this technology offers a novel approach to enhancing the performance of surgical navigation systems, providing innovative solutions for positioning requirements in medical practice.
- 26 September 2024
- Published by Springer Nature in Research Square
Open AccessResearch on Real-time Surgical Navigation System Based on Machine Learning and Fusion of Multimodal Sensors
In response to the significant challenge of occlusion in optical navigation, this paper proposes an innovative surgical navigation instrument positioning technique based on the fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data. By installing UWB positioning modules and IMU sensors on the surgical instrument arm, this study introduces a machine learning-based data fusion method to fully utilize UWB ranging and IMU high-precision roll and pitch angles, addressing the issues of attitude angles and Z-axis accuracy in UWB pose estimation. The method employs machine learning techniques to model the complex nonlinear relationships among three sets of UWB ranging values, two IMU attitude angles, and the position and yaw angle of the surgical instrument. Experimental results demonstrate that the proposed method achieves an average absolute error of less than 2cm in three-axis position and less than 1° in three-axis attitude angles. This surgical navigation instrument positioning technique exhibits significant advantages in realizing an efficient and accurate surgical navigation system. By optimizing the positioning accuracy of surgical instruments, this technology offers a novel approach to enhancing the performance of surgical navigation systems, providing innovative solutions for positioning requirements in medical practice.