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      Comparison of markerless and marker-based motion capture of gait kinematics in individuals with cerebral palsy and chronic stroke: A case study series

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          Abstract

          Background

          Three-dimensional (3D) motion analysis is an advanced tool used to quantify movement patterns in adults with chronic stroke and children with cerebral palsy. However, gold-standard marker-based systems have limitations for implementation in clinical settings. Markerless motion capture using Theia3D may provide a more accessible and clinically feasible alternative, but its accuracy is unknown in clinical populations. The purpose of this study was to quantify kinematic differences between marker-based and markerless motion capture systems in individuals with gait impairments.

          Methods

          Three adults with chronic stroke and three children with cerebral palsy completed overground walking trials while marker-based and markerless motion capture data were synchronously recorded. Time-series waveforms of 3D ankle, knee, hip, and trunk angles were stride normalized and compared. Root mean squared error, maximum peak, minimum peak, and range of motion were used to assess discrete point differences. Pearson’s correlation and coefficient of multiple correlation were computed to assess similarity between the time series joint angle waveforms from both systems.

          Results

          This study demonstrates that markerless motion capture using Theia3D produces good agreement with marker-based in the measurement of gait kinematics at most joints and anatomical planes in individuals with chronic stroke and cerebral palsy.

          Conclusions

          This is the first investigation to study the feasibility of Theia3D markerless motion capture for use in chronic stroke and cerebral palsy gait analysis. Our results indicate that markerless motion capture may be an acceptable tool to measure gait kinematics in clinical populations to provide clinicians with objective movement assessment data.

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          Most cited references22

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          Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association

          The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year’s worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year’s edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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            DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

            Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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              Human movement analysis using stereophotogrammetry. Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics.

              Estimating the effects of different sources of error on joint kinematics is crucial for assessing the reliability of human movement analysis. The goal of the present paper is to review the different approaches dealing with joint kinematics sensitivity to rotation axes and the precision of anatomical landmark determination. Consistent with the previous papers in this series, the review is limited to studies performed with video-based stereophotogrammetric systems. Initially, studies dealing with estimates of precision in determining the location of both palpable and internal anatomical landmarks are reviewed. Next, the effects of anatomical landmark position uncertainty on anatomical frames are shown. Then, methods reported in the literature for estimating error propagation from anatomical axes location to joint kinematics are described. Interestingly, studies carried out using different approaches reported a common conclusion: when joint rotations occur mainly in a single plane, minor rotations out of this plane are strongly affected by errors introduced at the anatomical landmark identification level and are prone to misinterpretation. Finally, attempts at reducing joint kinematics errors due to anatomical landmark position uncertainty are reported. Given the relevance of this source of errors in the determination of joint kinematics, it is the authors' opinion that further efforts should be made in improving the reliability of the joint axes determination.
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                Author and article information

                Contributors
                Journal
                Res Sq
                ResearchSquare
                Research Square
                American Journal Experts
                08 February 2023
                : rs.3.rs-2557403
                Affiliations
                University of Nebraska at Omaha
                University of Nebraska at Omaha
                University of Nebraska at Omaha
                University of Nebraska at Omaha
                Author notes

                Authors’ contributions

                DK and BK conceptualized the study. ES and FM collected and analyzed the data, and were major contributors in writing the manuscript. All authors were substantially involved in the interpretation of the data. DK and BK substantively revised the manuscript. All authors read and approved the final manuscript.

                Article
                10.21203/rs.3.rs-2557403
                10.21203/rs.3.rs-2557403/v1
                9934736
                36798184
                4784d6c6-287d-40cd-a424-d210885eb5cf

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

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                clinical gait,joint angle,markerless,walking,overground
                clinical gait, joint angle, markerless, walking, overground

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