IMU-based sensor-to-segment multiple calibration for upper limb joint angle measurement-a proof of concept.

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2019
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Abstract
A lot of attention has been paid to wearable inertial sensors regarded as an alternative solution for outdoor human motion tracking. Relevant joint angles can only be calculated from anatomical orientations, but they are negatively impacted by soft tissue artifact (STA) defined as skin motion with respect to the underlying bone; the accuracy of measured joint angle during movement is affected by the ongoing misalignment of the sensor. In this work, a new sensor-to-segment calibration using inertial measurement units is proposed. Inspired by the multiple calibration for a cluster of skin markers, it consists in performing first multiple static postures of the upper limb in all anatomical planes. The movements that affect sensor alignment are identified then alignment differences between sensors and segment frames are calculated for each posture and linearly interpolated. Experimental measurements were carried out on a mechanical model and on a subject who performed different movements of right elbow and shoulder. Multiple calibration showed significant improvement in joint angle measurement on the mechanical model as well as on human joint angle comparing to those obtained from attached sensors after technical calibration. During shoulder internal-external rotation, the maximal error value decreased more than 50% after correction. Graphical abstract Elbow flexion-extension joint angle values obtained from IMUs are well-corrected after applying multiple calibration procedure. Though shoulder internal-external rotation joint angle is more affected by soft tissue artifact, multiple calibration procedure improves the angle values obtained from IMUs.
Reference Key
zabat2019imubasedmedical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zabat, Mahdi;Ababou, Amina;Ababou, Noureddine;Dumas, Raphaël;
Journal Medical & biological engineering & computing
Year 2019
DOI 10.1007/s11517-019-02033-7
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Keywords Keywords not found

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