Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

Abstract

This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid-and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker-and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1°for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.

Development and Application of a Motion Analysis Protocol for the Kinematic Evaluation of Basic and Functional Hand and Finger Movements Using Motion Capture in a Clinical Setting—A Repeatability Study

“…Therefore, the placement of markers on the segments (FM2) seemed to be clearer and more straight forward. Interestingly, it was observed in a previous study that the accuracy of finger flexion angles with FM1 could benefit from skin movements during flexion [28], whereas the effects of systematic soft tissue displacement [27,28] for joint angles of FM2 are still unknown. As shown in this study, the choice of marker positioning, considering skin movement in particular, has a big impact on the repeatability of the resulting kinematics.…”

Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction

“…Segment length estimation using the vision-based 3D posture estimation systems is straightforward: each segment length is calculated based on the 3D position of the tracked markers at the each end of the segment. However, this process still suffers from inaccuracy due to imperfect marker placement and skin artifacts in MoCap systems [45] and model, sensor, and marker tracking uncertainties in markerless approaches [19].…”

“…However, as our model uses the fixed segment lengths and the MoCap skeleton uses variable segment lengths. MoCap calculates the segment lengths from the relative position of markers at each time, which are prone to change due to skin artifacts [45] and imperfect marker placement. Because of this, we see that the skeleton representing the re-targeted MoCap posture does not always follows the human arm in the video frames.…”

“…Traditionally, marker-based motion capture systems (MoCap) were the main approach in biomechanics studies due to their high frequency of operation and accuracy [46]. However, due to their personal, environmental, and technical constraints [12,32,45,53,61,62], new markerless methods are being developed that benefit from deep learning techniques to extract human posture from images and videos [8,23,43]. Among them, OpenPose [7] is a popular markerless method for human motion analysis due to its ease of use and integration.…”

MarkerLess Motion Capture: ML-MoCap, a low-cost modular multi-camera setup

“…Markers can introduce soft tissue artefacts as the markers move with and relative to the skin. For example, during finger movements, markers typically move 0.55 mm for each 10º of flexion around either the proximal interphalangeal (PIP) or the distal interphalangeal (DIP) joints [5]. Furthermore, participants move less naturally with markers attached [6].…”