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

Abstract

BackgroundThe 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.MethodsFive 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: ωmax = 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.ResultsThe 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.ConclusionsMIMU-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”.

Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch

“…HAR studies using wrist worn or smartwatch based inertial sensors have been performed (Nguyen et al 2015;Garcia-Ceja et al 2014;Yang et al 2008), however, these authors address classification of activities of daily living. Inertial sensors have also been studied for use in clinical shoulder evaluation (De Baets et al 2017;Korver et al 2014;Pichonnaz et al 2015), kinematic analysis (Crabolu et al 2017;Picerno et al 2015), range of motion measurement (Werner et al 2014), and upper extremity pose estimation (Shen et al 2016). To our knowledge, this study is the first to apply machine learning to wrist worn inertial sensor data for shoulder physiotherapy exercise recognition.…”

Functional estimation of bony segment lengths using magneto-inertial sensing: Application to the humerus

“…To effectively determine the humerus length, it is necessary that during the shoulder elevation, the elbow joint does not move and that both the shoulder and elbow movements are executed in the sagittal plane, avoiding any trunk swing ( Fig 1 ). In a previous study [ 16 ], a method called Null Acceleration Point (NAP ω ) was presented for the functional estimation of the shoulder CoR. Building on that result, herein, that method was used to estimate the radii r s and r e .…”

“…Building on that result, herein, that method was used to estimate the radii r s and r e . For a complete description of the algorithm that estimates the rotational radii, please refer to [ 16 , 25 ]. According to the NAP ω algorithm, the acceleration a of the origin of the MCS fixed with the forearm during a pure rotational motion can be expressed as follows: where r is the radius vector pointing from the MCS origin to the CoR and representing the CoR position, ω is the angular velocity, is the angular acceleration and K assumes the following form [ 25 ]: …”

“…Then, starting from the anatomical position, the subject was asked to execute five elbow flexion–extension (E FL-EX ) movements with an ROM of approximately 90°, maintaining the upper arm rigidly aligned to the trunk. The ROM of the shoulder elevation in the sagittal plane was chosen to guarantee a sufficiently wide humeral rotation with a limited scapulohumeral rhythm and spinal tilt rotation [ 16 , 29 , 30 ]. Both the S EL and E FL-EX movements were performed maintaining the wrist rigid as at the starting configuration.…”

“…In a previous study [ 16 ], we presented a functional method for estimating the centre of rotation of the glenohumeral joint using an MIMU placed on the upper arm. Building upon such a milestone, in this study, we propose an original method that is based on a functional approach for the automatic estimation of a bony segment length using a single MIMU attached to the subject’s wrist.…”