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Validation of two-dimensional video-based inference of finger kinematics with pose estimation

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant’s hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessmen...

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Published in:PloS one 2022-11, Vol.17 (11), p.e0276799-e0276799
Main Authors: Gionfrida, Letizia, Rusli, Wan M. R, Bharath, Anil A, Kedgley, Angela E
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description Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant’s hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a ‘gold standard’ marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. The accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland–Altman approach, and the coefficient of determination of linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03°) and MCP flexion (mean difference of 6.82°) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below 11° and on the order of that which is accepted clinically.
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subjects Analysis
Ankle
Biology and Life Sciences
Biomechanics
Camcorders
Cameras
Engineering and Technology
Finger
Fingers & toes
Gait
Hand
Hand (anatomy)
Kinematics
Laboratories
Markers
Medicine and Health Sciences
Motion capture
Occlusion
Parkinson's disease
Physical Sciences
Pose estimation
Technology application
Tracking
Walking
title Validation of two-dimensional video-based inference of finger kinematics with pose estimation
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