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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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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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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0276799</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0276799-e0276799</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Gionfrida et al. 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R</creatorcontrib><creatorcontrib>Bharath, Anil A</creatorcontrib><creatorcontrib>Kedgley, Angela E</creatorcontrib><title>Validation of two-dimensional video-based inference of finger kinematics with pose estimation</title><title>PloS one</title><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. 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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.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><doi>10.1371/journal.pone.0276799</doi><tpages>e0276799</tpages><orcidid>https://orcid.org/0000-0002-0992-6526</orcidid><oa>free_for_read</oa></addata></record> |
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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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