Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks

Abstract

Magnetic and inertial measurement units are an emerging technology to obtain 3D orientation of body segments in human movement analysis. In this respect, sensor fusion is used to limit the drift errors resulting from the gyroscope data integration by exploiting accelerometer and magnetic aiding sensors. The present study aims at investigating the effectiveness of sensor fusion methods under different experimental conditions. Manual and locomotion tasks, differing in time duration, measurement volume, presence/absence of static phases, and out-of-plane movements, were performed by six subjects, and recorded by one unit located on the forearm or the lower trunk, respectively. Two sensor fusion methods, representative of the stochastic (Extended Kalman Filter) and complementary (Non-linear observer) filtering, were selected, and their accuracy was assessed in terms of attitude (pitch and roll angles) and heading (yaw angle) errors using stereophotogrammetric data as a reference. The sensor fusion approaches provided significantly more accurate results than gyroscope data integration. Accuracy improved mostly for heading and when the movement exhibited stationary phases, evenly distributed 3D rotations, it occurred in a small volume, and its duration was greater than approximately 20 s. These results were independent from the specific sensor fusion method used. Practice guidelines for improving the outcome accuracy are provided.

Figure A1.

MIMU, marker and global frames are depicted during the static postures at the beginning of each trial (t₀) and in a generic time instant during the trial (t₁). The relative orientation of ULF with respect to MLF which corresponds to the orientation error is also indicated at t₁. For clarity reasons, the distance between the origins of MLF and ULF has been emphasized.

Figure 1.

MIMU location and ULF orientation during the static postures (corresponding to the selected GGF): X axis, antero-posterior and positive forward; Y axis, medio-lateral and positive to the right; Z axis, vertically aligned with the direction of the gravitational field vector and positive downwards.

Figure 2.

Framework of the SF method.

Figure 3.

Framework of the CF method.

Figure 4.

Heading and attitude errors (mean and one standard deviation) for the manual routine (on the left) and the locomotion (on the right) tasks. Significant differences among the INT, SF and CF methods are indicated with an asterisk.

Figure 5.

Heading and attitude errors (Δθ) of the INT, SF and CF methods plotted as a function of time for the locomotion task. The mean ± one standard deviation (SD) curves over the six participants are reported. Note the different scale of the axes of the ordinate in the two graphs.

Figure 6.

Heading angle as obtained during three complete “figures of eight” of one randomly selected locomotion trial. Angles obtained by the stereophotogrammetric system (solid line), and by numerical integration with (dashed line) and without (dotted line) bias correction (BC) are depicted.

Figure 7.

Heading angles as obtained by the stereophotogrammetric system (solid line) and by integration of the kinematic equations (dashed line) are depicted together with the values obtained by integration of the Z component of the angular velocity (dotted line) measured by the MIMU on the forearm, during one randomly selected manual routine trial.