Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All

Abstract

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units

“…A variety of methods can be adopted for this purpose, all of which are dependent on sensor fusion algorithms and determining the initial sensor-to-segment alignment via the implementation of calibration postures or functional movements that the participant must execute [ 5 ]. Acceptable levels of within- and between-participant, and within- and between-tester repeatability, can only be achieved with appropriate sensor fusion algorithm application and accurate and reliable calibration procedures [ 6 , 7 , 8 ]. A limited number of papers have attempted to directly estimate joint moments and ground reaction forces (GRF) using inertial sensors, with efforts to predict GRFs limited in accuracy and constrained to predictions of vertical component or peak values only.…”

Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing

“…Optimal working conditions can be considered the “best case scenario” in which the parameter values of each SFA are set to provide the lowest absolute orientation error (i.e., the best performance achievable) with respect to the orientation reference provided by a gold standard system [ 10 ]. However, this can be achieved only when the experiments are conducted inside the laboratory, thus precluding the use of MIMUs for an unconstrained monitoring of patients in their free-living environment, where the orientation reference is not available.…”

“…The same SFAs implemented in [ 10 ] are considered to test the validity of the RCM. The MATLAB (R2020a, The MathWorks Inc., Natick, MA, USA) implementations for each SFA were made available on GitHub (at , accessed on 21 June 2021) and also on MATLAB Exchange (at , accessed on 4 May 2021).…”

“…However, the latter approach may be unfeasible since gold standard such as stereophotogrammetric system (SP) are rarely available out of specialized human movement laboratories. Recent works have confirmed the crucial role played by the criteria used to select appropriate parameter values [ 8 , 9 , 10 , 11 ]. It has also been shown that several intrinsic and extrinsic factors should be considered when performing this selection.…”

Moving on: measuring movement remotely after stroke

“…The Kalman filter is essentially a Bayesian fusion model, that fuses different sources of information whilst assuming that the noise associated with each information is known, and thereby, an optimal estimate can be derived. Other sensor fusion techniques may be employed as long as the dependent parameters are tuned well (Caruso et al, 2021). Thus, the relative estimations of foot and CoM distances within the PGL are an optimal estimate.…”