Comparative assessment of the effects of different error sources on the estimate of 3D orientation using magnetic-inertial measurement units

Abstract

Main topics: Technical developments in movement science; Mathematical simulation in human mov. science Introduction and aim: The determination of the accurate 3D orientation of a body segment is a crucial aspect in clinical movement analysis [1]. Among motion capture technologies, wearable sensors, suchasmagnetic-inertialmeasurementunits (MIMUs), are gaining popularity. Several methods were developed to estimate 3D orientation from MIMU data, in order to effectively exploit the complementary properties of inertial (gyroscopes and accelerometers) and magnetic sensors embedded in the MIMUs [2]. It is well known that these sensors are affected by several error sources, including measurement noise, bias instability, calibration errors, ferromagneticdisturbances, external accelerations [3]. Thepurpose of the present study is to assess the contribution of the sources of error associated to each sensor to the uncertainty affecting the 3D orientation estimation, during a consolidated clinical test. Patients/materials andmethods:ATimedUpandGo test (TUG) was performed by ten healthy subjects (5M, 5 F, 28 5 y). OneMIMU (Opal, APDM Inc.) was secured to the lower back of each subject and angular velocity, acceleration and local magnetic field vector data were collected. The trajectories of four markers attached to theMIMUwere trackedby a stereophotogrammetric system (Vicon MX3), and the orientation of a marker-based frame was obtained andconsideredas reference. Thesemarker trajectorieswereused to calculate the same variablesmeasured by theMIMU.Measured and simulated MIMU data were then combined, as detailed in Table 1, obtaining five different scenarios. Each signal combination was fed as an input of an Extended Kalman Filter (EKF) [2] to estimate the 3D MIMU orientation. The rationale was to assess, both individually and jointly, the effect of the errors associated to the EKFmodel, as well as to each MIMU sensor. The accuracy of the obtained orientations was then evaluated in terms of orientation error [2]. A Kruskal–Wallis testwithpost-hocDunncomparisons ( =0.05)was performed to investigate whether significance differences existed among the obtained orientation errors. Results: Results (mean one standard deviation) about the error contribution associated to the EKF model and to the MIMU sensors