An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors
Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2018
|
| Online Access: | http://hdl.handle.net/1721.1/116622 https://orcid.org/0000-0002-6582-0865 https://orcid.org/0000-0003-1672-8189 https://orcid.org/0000-0002-0119-1617 |
| _version_ | 1826199649198800896 |
|---|---|
| author | McGrath, Timothy Michael Fineman, Richard Andres Stirling, Leia A. |
| author2 | Institute for Medical Engineering and Science |
| author_facet | Institute for Medical Engineering and Science McGrath, Timothy Michael Fineman, Richard Andres Stirling, Leia A. |
| author_sort | McGrath, Timothy Michael |
| collection | MIT |
| description | Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study (<i>n</i> = 15) with an absolute root-mean-square-error (RMSE) of 9.24<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula> and a zero-mean RMSE of 3.49<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data. |
| first_indexed | 2024-09-23T11:23:18Z |
| format | Article |
| id | mit-1721.1/116622 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:23:18Z |
| publishDate | 2018 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1166222022-10-01T03:17:55Z An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors McGrath, Timothy Michael Fineman, Richard Andres Stirling, Leia A. Institute for Medical Engineering and Science Massachusetts Institute of Technology. Department of Aeronautics and Astronautics McGrath, Timothy Michael Fineman, Richard Andres Stirling, Leia A. Inertial measurement units (IMUs) have been demonstrated to reliably measure human joint angles—an essential quantity in the study of biomechanics. However, most previous literature proposed IMU-based joint angle measurement systems that required manual alignment or prescribed calibration motions. This paper presents a simple, physically-intuitive method for IMU-based measurement of the knee flexion/extension angle in gait without requiring alignment or discrete calibration, based on computationally-efficient and easy-to-implement Principle Component Analysis (PCA). The method is compared against an optical motion capture knee flexion/extension angle modeled through OpenSim. The method is evaluated using both measured and simulated IMU data in an observational study (<i>n</i> = 15) with an absolute root-mean-square-error (RMSE) of 9.24<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula> and a zero-mean RMSE of 3.49<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>. Variation in error across subjects was found, made emergent by the larger subject population than previous literature considers. Finally, the paper presents an explanatory model of RMSE on IMU mounting location. The observational data suggest that RMSE of the method is a function of thigh IMU perturbation and axis estimation quality. However, the effect size for these parameters is small in comparison to potential gains from improved IMU orientation estimations. Results also highlight the need to set relevant datums from which to interpret joint angles for both truth references and estimated data. National Science Foundation (U.S.) (GRFP) National Science Foundation (U.S.) (IIS-1453141) 2018-06-26T15:06:15Z 2018-06-26T15:06:15Z 2018-06 2018-06-25T07:43:21Z Article http://purl.org/eprint/type/JournalArticle 1424-8220 http://hdl.handle.net/1721.1/116622 McGrath, Timothy, Richard Fineman and Leia Stirling. "An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors." Sensors 2018, 18(6), 1882. https://orcid.org/0000-0002-6582-0865 https://orcid.org/0000-0003-1672-8189 https://orcid.org/0000-0002-0119-1617 http://dx.doi.org/10.3390/s18061882 Sensors Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | McGrath, Timothy Michael Fineman, Richard Andres Stirling, Leia A. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title | An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title_full | An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title_fullStr | An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title_full_unstemmed | An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title_short | An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors |
| title_sort | auto calibrating knee flexion extension axis estimator using principal component analysis with inertial sensors |
| url | http://hdl.handle.net/1721.1/116622 https://orcid.org/0000-0002-6582-0865 https://orcid.org/0000-0003-1672-8189 https://orcid.org/0000-0002-0119-1617 |
| work_keys_str_mv | AT mcgrathtimothymichael anautocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors AT finemanrichardandres anautocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors AT stirlingleiaa anautocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors AT mcgrathtimothymichael autocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors AT finemanrichardandres autocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors AT stirlingleiaa autocalibratingkneeflexionextensionaxisestimatorusingprincipalcomponentanalysiswithinertialsensors |