Human joint motion data capture and fusion based on wearable sensors
Abstract The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high l...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Autonomous Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43684-025-00098-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704339509084160 |
|---|---|
| author | Hua Wang |
| author_facet | Hua Wang |
| author_sort | Hua Wang |
| collection | DOAJ |
| description | Abstract The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high latency. Consequently, in order to overcome the limitations of existing wearable sensors in data capture and fusion, the study initially constructed a model of the human joint and bone by combining the quaternion method and root bone human forward kinematics through mathematical modeling. Subsequently, the sensor data calibration was optimized, and the Madgwick algorithm was introduced to address the resulting issues. Finally, a novel human joint motion data capture and fusion model was proposed. The experimental results indicated that the maximum mean error and root mean square error of yaw angle of this new model were 1.21° and 1.17°, respectively. The mean error and root mean square error of pitch angle were maximum 1.24° and 1.19°, respectively. The maximum knee joint and elbow joint data capture errors were 3.8 and 6.1, respectively. The suggested approach, which offers a new path for technological advancement in this area, greatly enhances the precision and dependability of human motion capture, which has a broad variety of application possibilities. |
| format | Article |
| id | doaj-art-47d0bd7c252f4f42b41a45fa44f977b2 |
| institution | DOAJ |
| issn | 2730-616X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Autonomous Intelligent Systems |
| spelling | doaj-art-47d0bd7c252f4f42b41a45fa44f977b22025-08-20T03:16:47ZengSpringerAutonomous Intelligent Systems2730-616X2025-05-015111210.1007/s43684-025-00098-wHuman joint motion data capture and fusion based on wearable sensorsHua Wang0Department of Sports, Suzhou University of Science and TechnologyAbstract The field of human motion data capture and fusion has a broad range of potential applications and market opportunities. The capture of human motion data for wearable sensors is less costly and more convenient than other methods, but it also suffers from poor data capture accuracy and high latency. Consequently, in order to overcome the limitations of existing wearable sensors in data capture and fusion, the study initially constructed a model of the human joint and bone by combining the quaternion method and root bone human forward kinematics through mathematical modeling. Subsequently, the sensor data calibration was optimized, and the Madgwick algorithm was introduced to address the resulting issues. Finally, a novel human joint motion data capture and fusion model was proposed. The experimental results indicated that the maximum mean error and root mean square error of yaw angle of this new model were 1.21° and 1.17°, respectively. The mean error and root mean square error of pitch angle were maximum 1.24° and 1.19°, respectively. The maximum knee joint and elbow joint data capture errors were 3.8 and 6.1, respectively. The suggested approach, which offers a new path for technological advancement in this area, greatly enhances the precision and dependability of human motion capture, which has a broad variety of application possibilities.https://doi.org/10.1007/s43684-025-00098-wMadgwick Wearable sensorsJointsData captureFusionMadgwick |
| spellingShingle | Hua Wang Human joint motion data capture and fusion based on wearable sensors Autonomous Intelligent Systems Madgwick Wearable sensors Joints Data capture Fusion Madgwick |
| title | Human joint motion data capture and fusion based on wearable sensors |
| title_full | Human joint motion data capture and fusion based on wearable sensors |
| title_fullStr | Human joint motion data capture and fusion based on wearable sensors |
| title_full_unstemmed | Human joint motion data capture and fusion based on wearable sensors |
| title_short | Human joint motion data capture and fusion based on wearable sensors |
| title_sort | human joint motion data capture and fusion based on wearable sensors |
| topic | Madgwick Wearable sensors Joints Data capture Fusion Madgwick |
| url | https://doi.org/10.1007/s43684-025-00098-w |
| work_keys_str_mv | AT huawang humanjointmotiondatacaptureandfusionbasedonwearablesensors |