Estimation of the center of rotation using wearable magneto-inertial sensors

Crabolu, Michele; Pani, Danilo; Raffo, Luigi; Cereatti, Andrea

doi:10.1016/j.jbiomech.2016.11.046

Cited by 21 publications

(24 citation statements)

References 22 publications

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“…The following algorithms were implemented and compared.The original algorithm proposed by Crabolu et al [25, 26], based on the use of a single MIMU, hereafter named Null Acceleration Point (), where the superscript indicates the number of MIMUs used;A variant of the algorithm , which includes a criterion based on the analysis of the angular velocity for selecting the samples to be used for the estimation of the GHJC, hereafter named ;A variant of algorithm , which uses two MIMUs and exploits the scapulae acceleration information for selecting the samples to be used for the estimation of the GHJC, hereafter named ;The original algorithm proposed by Seel et al [27], based on the use of two MIMUs, referred to as Symmetrical Specific Force Center ();A combination of the two methods 1a and 2, based on the use of two MIMUs, that we named Symmetrical Acceleration Center (). …”

Section: Methodsmentioning

confidence: 99%

“…The original algorithm proposed by Crabolu et al [25, 26], based on the use of a single MIMU, hereafter named Null Acceleration Point (), where the superscript indicates the number of MIMUs used;…”

Section: Methodsmentioning

confidence: 99%

“…It was proved that to reduce SNR associated to angular velocity signals [26], slow calibration movements should be avoided since they lead to worse results compared to fast movements [25, 26]. To take advantage of this previous observation, only data samples characterized by a magnitude of the angular velocity higher than an empirically chosen threshold, equal to 0.5 rad/s, were included in the least-square solution computation.…”

Section: Methodsmentioning

confidence: 99%

“…The MIMU-based GHJC position estimate is expressed with respect to the MCS [26], which is aligned to the edges of the MIMU housing. In order to obtain the gold standard position of the GHJC with respect to the MCS using MRI, a non-ferromagnetic phantom of the MIMU was designed (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies demonstrated that it is possible to estimate the CoR position of a spherical joint using inertial sensors [24–26]. However, the methods validation was carried out on a mechanical analogue of a generic spherical human joint by manually moving a segment with respect to a stationary frame.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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”.

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