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      Study of the Distortion of the Indirect Angular Measurements of the Calcaneus Due to Perspective: In Vitro Testing

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          Abstract

          The study of the foot is relevant in kinematic analyses of gait. Images captured through a lens can be subjected to various aberrations or distortions that affect the measurements. An in vitro study was performed with a rearfoot simulator to compare the apparent degrees (photographed) with the real ones (placed in the simulator) in the plane of the rearfoot’s orientation, according to variations in the capture angle in other planes of space (the sagittal plane and transverse plane—the latter determined by the foot progression angle). The following regression formula was calculated to correct the distortion of the image: real frontal plane = 0.045 + (1.014 × apparent frontal plane) − (0.018 × sagittal plane × foot progression angle). Considering the results of this study, and already knowing its angle in the transverse and sagittal planes, it is possible to determine the angle of a simulated calcaneus with respect to the ground in the frontal plane, in spite of distortions caused by perspective and the lack of perpendicularity, by applying the above regression formula. The results show that the angular measurements of a body segment made on frames can produce erroneous data due to the variation in the perspective from which the image is taken. This distortion must be considered when determining the real values of the measurements.

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          Application of Student's t -test, Analysis of Variance, and Covariance

          Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To identify that significant pair(s), we use multiple comparisons. In ANOVA, when using one categorical independent variable, it is called one-way ANOVA, whereas for two categorical independent variables, it is called two-way ANOVA. When using at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA. When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.
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            Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

            Background Understanding the effects of gait speed on biomechanical variables is fundamental for a proper evaluation of alterations in gait, since pathological individuals tend to walk slower than healthy controls. Therefore, the aim of the study was to perform a systematic review of the effects of gait speed on spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces in healthy children, young adults, and older adults. Methods A systematic electronic search was performed on PubMed, Embase, and Web of Science databases to identify studies published between 1980 and 2019. A modified Quality Index was applied to assess methodological quality, and effect sizes with 95% confidence intervals were calculated as the standardized mean differences. For the meta-analyses, a fixed or random effect model and the statistical heterogeneity were calculated using the I 2 index. Results Twenty original full-length studies were included in the final analyses with a total of 587 healthy individuals evaluated, of which four studies analyzed the gait pattern of 227 children, 16 studies of 310 young adults, and three studies of 59 older adults. In general, gait speed affected the amplitude of spatiotemporal gait parameters, joint kinematics, joint kinetics, and ground reaction forces with a decrease at slow speeds and increase at fast speeds in relation to the comfortable speed. Specifically, moderate-to-large effect sizes were found for each age group and speed: children (slow, − 3.61 to 0.59; fast, − 1.05 to 2.97), young adults (slow, − 3.56 to 4.06; fast, − 4.28 to 4.38), and older adults (slow, − 1.76 to 0.52; fast, − 0.29 to 1.43). Conclusions This review identified that speed affected the gait patterns of different populations with respect to the amplitude of spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces. Specifically, most of the values analyzed decreased at slower speeds and increased at faster speeds. Therefore, the effects of speed on gait patterns should also be considered when comparing the gait analysis of pathological individuals with normal or control ones. Electronic supplementary material The online version of this article (10.1186/s13643-019-1063-z) contains supplementary material, which is available to authorized users.
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              Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

              The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson’s disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5–100%, sensitivity of 83.3–100% and specificity of 82–100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8–100%, sensitivity of 92.5–100% and specificity of 88–100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                07 April 2021
                April 2021
                : 21
                : 8
                : 2585
                Affiliations
                [1 ]Department of Podiatry, Faculty of Nursing, Physiotherapy and Podiatry, Universidad de Sevilla, C/Avice-na s/n, 41009 Seville, Spain; espinosa@ 123456us.es (I.E.-M.); ipalomo@ 123456us.es (I.C.P.-T.); jmcastillo@ 123456us.es (J.M.C.-L.); gdominguez@ 123456us.es (G.D.-M.)
                [2 ]Department of Nursing, Faculty of Nursing, Physiotherapy and Podiatry, Universidad de Sevilla, C/Avenzoar nº6, 41009 Seville, Spain; joserafael@ 123456us.es
                Author notes
                [* ]Correspondence: mreina1@ 123456us.es ; Tel.: +34-954486544
                Author information
                https://orcid.org/0000-0001-9316-8942
                https://orcid.org/0000-0001-9901-8937
                https://orcid.org/0000-0002-8962-3540
                https://orcid.org/0000-0001-8549-7703
                Article
                sensors-21-02585
                10.3390/s21082585
                8067713
                bc1776a0-61a8-4ca7-999a-2f0b7481e052
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 16 March 2021
                : 06 April 2021
                Categories
                Article

                Biomedical engineering
                biomechanics,instrumentation,image processing,computer-assisted,foot,gait
                Biomedical engineering
                biomechanics, instrumentation, image processing, computer-assisted, foot, gait

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