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

Abstract

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.

Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review

“…These issues still remain an open question in sensor-to-segment calibrations [ 86 , 87 , 88 ]. In estimating the joint center position, two methods of estimating the sensor-to-joint vector as a time-varying one have been proposed to consider soft tissue artifacts, but they have a limitation in that they only work in motions where the joint center is moveless due to kinematic constraints [ 89 , 90 ]. These unresolved problems related to the segment coordinate systems and the joint center positions will be one future research direction.…”

Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

“…The first approach is to obtain an average location of joints with respect to the sensors, which has been validated for the upper-and lower-limbs [178], [179], respectively, but require specific calibration motions. The second method consists in estimating an adaptive position vector, considering the changes of the location of IMUs due to soft tissue artifacts [180]- [183], which has been validated for the calibration of hips performing leg circles [182]. These proposals assume that the joints are fixed, but it is not the case in all the activities in the daily living.…”

A database of physical therapy exercises with variability of execution collected by wearable sensors

“…4). With this motion, the COR can be estimated using algorithms as different works propose [29][30][31] . www.nature.com/scientificdata www.nature.com/scientificdata/ • Hip frontal flex-extensions: standing, keeping the hip still and maintaining the legs completely straight again, the volunteers moved this leg in a forward-backwards motion (see Hip axis ⊥ sagittal in Fig.…”