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Correction published on 28 March 2019, see Sensors 2019, 19(7), 1504.
Open AccessArticle

An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors

1
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
2
Harvard-MIT Division of Health Sciences & Technology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
3
Institute for Medical Engineering Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1882; https://doi.org/10.3390/s18061882
Received: 27 April 2018 / Revised: 3 June 2018 / Accepted: 6 June 2018 / Published: 8 June 2018
(This article belongs to the Special Issue Wearable Biomedical Sensors 2019)
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 (n = 15) with an absolute root-mean-square-error (RMSE) of 9.24 and a zero-mean RMSE of 3.49. 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. View Full-Text
Keywords: inertial sensor; IMU; gait; knee flexion; knee extension; principle component analysis inertial sensor; IMU; gait; knee flexion; knee extension; principle component analysis
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MDPI and ACS Style

McGrath, T.; Fineman, R.; Stirling, L. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors 2018, 18, 1882. https://doi.org/10.3390/s18061882

AMA Style

McGrath T, Fineman R, Stirling L. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors. 2018; 18(6):1882. https://doi.org/10.3390/s18061882

Chicago/Turabian Style

McGrath, Timothy; Fineman, Richard; Stirling, Leia. 2018. "An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors" Sensors 18, no. 6: 1882. https://doi.org/10.3390/s18061882

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