In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and...

The human gleno-humeral joint is normally represented as a spherical hinge and its center of rotation is used to construct humerus anatomical axes and as reduction point for the computation of the internal joint moments. The position of the gleno-humeral joint center (GHJC) can be estimated by recording ad hoc shoulder joint movement following a functional approach. In the last years, extensive research has been conducted to improve GHJC estimate as obtained from positioning systems such as stereo-photogrammetry or electromagnetic tracking. Conversely, despite the growing interest for wearable technologies in the field of human movement analysis, no studies investigated the problem of GHJC estimation using miniaturized magneto-inertial measurement units (MIMUs). The aim of this study was to evaluate both accuracy and precision of the GHJC estimation as obtained using a MIMU-based methodology and a functional approach.

Five different functional methods were implemented and comparatively assessed under different experimental conditions (two types of shoulder motions: cross and star type motion; two joint velocities: ω_max = 90°/s, 180°/s; two ranges of motion: Ɵ = 45°, 90°). Validation was conducted on five healthy subjects and true GHJC locations were obtained using magnetic resonance imaging.

The best performing methods (NAP and SAC) showed an accuracy in the estimate of the GHJC between 20.6 and 21.9 mm and repeatability values between 9.4 and 10.4 mm. Methods performance did not show significant differences for the type of arm motion analyzed or a reduction of the arm angular velocity (180°/s and 90°/s). In addition, a reduction of the joint range of motion (90° and 45°) did not seem to influence significantly the GHJC position estimate except in a few subject-method combinations.

MIMU-based functional methods can be used to estimate the GHJC position in vivo with errors of the same order of magnitude than those obtained using traditionally stereo-photogrammetric techniques. The methodology proposed seemed to be robust under different experimental conditions. The present paper was awarded as "SIAMOC Best Methodological Paper 2016".