Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework
Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference,...
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| Format: | Article |
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MDPI AG
2021
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| Online Access: | https://hdl.handle.net/1721.1/129345 |
| _version_ | 1811081774242988032 |
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| author | McGrath, Timothy Michael Stirling, Leia |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics McGrath, Timothy Michael Stirling, Leia |
| author_sort | McGrath, Timothy Michael |
| collection | MIT |
| description | Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints. |
| first_indexed | 2024-09-23T11:52:16Z |
| format | Article |
| id | mit-1721.1/129345 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:52:16Z |
| publishDate | 2021 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | mit-1721.1/1293452022-09-27T22:31:29Z Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework McGrath, Timothy Michael Stirling, Leia Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints. National Science Foundation (NSF) (Grant IIS-1453141) 2021-01-08T15:25:29Z 2021-01-08T15:25:29Z 2020-12 2020-11 2020-12-10T14:11:23Z Article http://purl.org/eprint/type/JournalArticle 1424-8220 https://hdl.handle.net/1721.1/129345 McGrath, Timothy and Leia Stirling. "Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework." Sensors 20, 23 (December 2020): 6887 © 2020 The Authors http://dx.doi.org/10.3390/s20236887 Sensors Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf MDPI AG Multidisciplinary Digital Publishing Institute |
| spellingShingle | McGrath, Timothy Michael Stirling, Leia Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title | Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title_full | Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title_fullStr | Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title_full_unstemmed | Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title_short | Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework |
| title_sort | body worn imu human skeletal pose estimation using a factor graph based optimization framework |
| url | https://hdl.handle.net/1721.1/129345 |
| work_keys_str_mv | AT mcgrathtimothymichael bodywornimuhumanskeletalposeestimationusingafactorgraphbasedoptimizationframework AT stirlingleia bodywornimuhumanskeletalposeestimationusingafactorgraphbasedoptimizationframework |