Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist
Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to diff...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7053 |
| _version_ | 1797583254489923584 |
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| author | Howard Chen Mark C. Schall Scott M. Martin Nathan B. Fethke |
| author_facet | Howard Chen Mark C. Schall Scott M. Martin Nathan B. Fethke |
| author_sort | Howard Chen |
| collection | DOAJ |
| description | Joint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively. |
| first_indexed | 2024-03-10T23:36:15Z |
| format | Article |
| id | doaj.art-0ff67723b3694892b429bbedd79ab508 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:36:15Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0ff67723b3694892b429bbedd79ab5082023-11-19T02:56:10ZengMDPI AGSensors1424-82202023-08-012316705310.3390/s23167053Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and WristHoward Chen0Mark C. Schall1Scott M. Martin2Nathan B. Fethke3Industrial & Systems Engineering and Engineering Management Department, University of Alabama in Huntsville, Huntsville, AL 35899, USADepartment of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USADepartment of Mechanical Engineering, Auburn University, Auburn, AL 36849, USADepartment of Occupational & Environmental Health, The University of Iowa, Iowa City, IA 52242, USAJoint angles of the lower extremities have been calculated using gyroscope and accelerometer measurements from inertial measurement units (IMUs) without sensor drift by leveraging kinematic constraints. However, it is unknown whether these methods are generalizable to the upper extremity due to differences in motion dynamics. Furthermore, the extent that post-processed sensor fusion algorithms can improve measurement accuracy relative to more commonly used Kalman filter-based methods remains unknown. This study calculated the elbow and wrist joint angles of 13 participants performing a simple ≥30 min material transfer task at three rates (slow, medium, fast) using IMUs and kinematic constraints. The best-performing sensor fusion algorithm produced total root mean square errors (i.e., encompassing all three motion planes) of 6.6°, 3.6°, and 2.0° for the slow, medium, and fast transfer rates for the elbow and 2.2°, 1.7°, and 1.5° for the wrist, respectively.https://www.mdpi.com/1424-8220/23/16/7053inertial measurement unitsinertial motion capturebiomechanical motion analysisoptimal estimationbiomechanicssmoothers |
| spellingShingle | Howard Chen Mark C. Schall Scott M. Martin Nathan B. Fethke Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist Sensors inertial measurement units inertial motion capture biomechanical motion analysis optimal estimation biomechanics smoothers |
| title | Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist |
| title_full | Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist |
| title_fullStr | Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist |
| title_full_unstemmed | Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist |
| title_short | Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist |
| title_sort | drift free joint angle calculation using inertial measurement units without magnetometers an exploration of sensor fusion methods for the elbow and wrist |
| topic | inertial measurement units inertial motion capture biomechanical motion analysis optimal estimation biomechanics smoothers |
| url | https://www.mdpi.com/1424-8220/23/16/7053 |
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