Adaptive IMU-based Calibration of the Center of Joints for Movement Analysis: One Case Study

Villa, Sara García de; Martín, Ana Jiménez; Domínguez, Juan Jesús García

doi:10.1109/memea49120.2020.9137135

Cited by 5 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notice also that ṙk is not related to any measured magnitude, so it is not directly updated, but used as a parameter of adjustment of the EKF. The use of the derivative of r in the state vector of the EKF is one of the main differences between ArVE d and ArVE, our initial approach proposed in [24], differentiated with the subindex d.…”

Section: A Proposed Algorithm: Arve Dmentioning

confidence: 99%

“…The amplitudes of the movements around the x, y and z axes are 17 • , 9 • and 3 • , respectively, and the motion of the pendulum around the x and y axes lasts 1 s; and 1.5 s around the z axis. The parameters of the simulated motions are set according to the motions observed during the lower-limb calibration observed in [31], as we do in the simulations reported in [24]. Fig.…”

Section: A Simulation Of a Spherical Pendulummentioning

confidence: 99%

“…It is remarkable that all aforementioned inertial approaches entail different signal pre-processing to reduce noise in the IMU data and to estimate the angular acceleration, ω, a common parameter required in these methods. In contrast, our initial proposed method, called ArVE, estimates an r at each time instant without signal pre-processing [24]. In the initial evaluation, ArVE provides average errors of 1.5 mm and 6.0 mm in the fixed and changing r cases, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Bias is not considered since simulations and tests are short enough in time to be affected by it, as done in [18] and because the estimation of r does not include integration, so its estimations are insensitive to bias, according to [20]. In order to provide more significant results than in our previous work [24], we carry out 100 tests for each experiment.…”

Section: A Simulation Of a Spherical Pendulummentioning

confidence: 99%

“…Our proposal is called ArVE d , that stands for Adaptive r Vector Estimator. This algorithm is based on the method introduced in [24]. To evaluate the performance of our proposal, we compare ArVE d with the approach described in [17], used as a reference to estimate an average r, hereinafter MrVS, that stands for Mean r Vector least-Squares-based estimator.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Novel IMU-Based Adaptive Estimator of the Center of Rotation of Joints for Movement Analysis

Villa

Martín

Domínguez

2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

The location of the center of rotation (COR) of joints is a key parameter in multiple applications of human motion analysis. The aim of this work was to propose a novel realtime estimator of the center of fixed joints using an inertial measurement unit (IMU). Since the distance to this center commonly varies during the joint motion due to soft tissue artifacts (STA), our approach is aimed at adapting to these small variations when the COR is fixed. Our proposal, called ArVE d , to the best of our knowledge, is the first real-time estimator of the IMU-joint center vector based on one IMU. Previous works are off-line and require a complete measurement batch to be solved and most of them are not tested on the real scenario. The algorithm is based on an Extended Kalman Filter (EKF) that provides an adaptive vector to STA motion variations at each time instant, without requiring a pre-processing stage to reduce the level of noise. ArVE d has been tested through different experiments, including synthetic and real data. The synthetic data are obtained from a simulated spherical pendulum whose COR is fixed, considering both a constant and a variable IMU-joint vector, that simulates translational IMU motions due to STA. The results prove that ArVE d is adapted to obtain a vector per sample with an accuracy of 6.8 ± 3.9 mm on the synthetic data, that means an error lower than 3.5 % of the simulated IMU-joint vector. Its accuracy is also tested on the real scenario estimating the COR of the hip of 5 volunteers using as reference the results from an optical system. In this case, ArVE d gets an average error of 9.5 % of the real vector value. In all the experiments, ArVE d outperforms the published results of the reference algorithms.

show abstract

Section: A Proposed Algorithm: Arve Dmentioning

confidence: 99%

Section: A Simulation Of a Spherical Pendulummentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%