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      Normal variation in pelvic roll motion pattern during straight-line trot in hand in warmblood horses

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          Abstract

          In horses, hip hike asymmetry, i.e. left–right difference in hip upwards movement during hind limb protraction in trot, is a crucial lameness sign. Vertical hip movements are complex, influenced by both pelvic roll and pelvic vertical motion. Veterinarians find it challenging to identify low-grade lameness, and knowledge of normal variation is a prerequisite for discerning abnormalities. This study, which included 100 clinically sound Warmblood horses, aimed to describe normal variation in pelvic roll stride patterns. Data were collected during straight-line trot in hand using optical motion capture. Stride-segmented pelvic roll data, normalised with respect to time (0–100% of the stride) and amplitude (± 0.5 of horse average stride range of motion), were modelled as a linear combination of sine and cosine curves. A sine curve with one period per stride and a cosine curve with three periods per stride explained the largest proportions of roll motion: model estimate 0.335 (p < 0.01) and 0.138 (p < 0.01), respectively. Using finite mixture models, the horses could be separated into three groups sharing common pelvic roll characteristics. In conclusion, pelvic roll motion in trot follows a similar basic pattern in most horses, yet there is significant individual variation in the relative prominence of the most characteristic features.

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          FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters

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            Explaining the unique nature of individual gait patterns with deep learning

            Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait.
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              A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods.

              Tom Chau (2001)
              In recent years, several new approaches to gait data analysis have been explored, including fuzzy systems, multivariate statistical techniques and fractal dynamics. Through a critical survey of recent gait studies, this paper reviews the potential of these methods to strengthen the gait laboratory's analytical arsenal. It is found that time-honoured multivariate statistical methods are the most widely applied and understood. Although initially promising, fuzzy and fractal analyses of gait data remain largely unknown and their full potential is yet to be realized. The trend towards fusing multiple techniques in a given analysis means that additional research into the application of these two methods will benefit gait data analysis.
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                Author and article information

                Contributors
                anna.bystrom@slu.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 October 2023
                10 October 2023
                2023
                : 13
                : 17117
                Affiliations
                [1 ]Department of Animal Environment and Health, Section of Ethology and Animal Welfare, Swedish University of Agricultural Sciences, ( https://ror.org/02yy8x990) Uppsala, Sweden
                [2 ]Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, ( https://ror.org/04pp8hn57) Utrecht, The Netherlands
                [3 ]Department of Companion Animal Clinical Sciences, Faculty of Veterinary Medicine, Equine Teaching Hospital, Norwegian University of Life Sciences, ( https://ror.org/04a1mvv97) Oslo, Norway
                [4 ]Tierklinik Lüesche GmbH, Lüesche, Germany
                [5 ]Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, ( https://ror.org/02yy8x990) Uppsala, Sweden
                Article
                44223
                10.1038/s41598-023-44223-2
                10564842
                37816848
                4e4b4b25-57a8-4f03-9053-8dd93990818c
                © Springer Nature Limited 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 June 2023
                : 5 October 2023
                Funding
                Funded by: Swedish-Norwegian Foundation for Equine Research
                Award ID: H-17-47-304
                Funded by: Swedish University of Agricultural Sciences
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                biomechanics,statistics
                Uncategorized
                biomechanics, statistics

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