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

Abstract

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.

Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy

“…To guaranty reproducible measure and be independent of the IMU location on each segment, lower limbs sensors were automatically aligned with the functional axis of the movement. To this end, assuming that the main angular rotation during gait occurs around the medio-lateral axis of each segment, principal component analysis (PCA) was applied on angular velocity to assess the pitch component of the shanks and thighs rotation 44,45 . For each trial, the norm of acceleration of the chest was computed, to preclude wrong axis selection resulting from potential misalignment of the sensor with regard to the chest.…”

Multidimensional Measures of Physical Activity and Their Association with Gross Motor Capacity in Children and Adolescents with Cerebral Palsy

“…Data were post-processed and analyzed using Matlab R2017 software (Mathworks, Natick, MA, USA). To auto-calibrate the sensors’ axis, a principal component analysis (PCA) was used [ 34 ]. The oriented pitch (i.e., in the sagittal plane) angular velocity of the shanks were used to detect walking episodes [ 35 ].…”

A Practical Gait Feedback Method Based on Wearable Inertial Sensors for a Drop Foot Assistance Device

“…Its accuracy relies upon the performance of the movements. Sensor-to-segment identification approaches have been proposed to determine the local joint axes and position coordinates by exploring the kinematics of the joint from arbitrary motions [10], [32], [33]. The automatic calibration approaches does not require precise placement of sensors attached to the body making the system more robust and practical for wearable applications.…”