Abstract
Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
Highlights
Background & SummaryIn recent decades, researchers relied on laboratory equipment and computational methods to track human movements[1]
It is not known how well these model-based alignment methods are able to identify the direction of underlying joint axes that relate with anatomical landmarks, as defined by the clinical definitions[8,9]
It is not straightforward to evaluate alignment models when errors from inertial sensor orientation estimation and kinematic cross-talk due to mis-alignments are intertwined[12] and possibly disturbed by skin motion artifacts, which led researchers to question an Optical motion capture (OMC) as an appropriate reference[12,13,21]
Summary
Background & SummaryIn recent decades, researchers relied on laboratory equipment and computational methods to track human movements[1]. We focus on the tibiofemoral (TF) joint that is most studied for inertial-sensor-to-bone alignment[7] and report a rich dataset of dynamic movements on a cadaver that were recorded with multi-modal measurements including trajectories of optical markers and virtual (through volumetric computed tomography (CT) scanning) anatomical markers, reference joint kinematics and inertial measurements (Fig. 1d).
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