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      The nature and extent of upper limb associated reactions during walking in people with acquired brain injury

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          Abstract

          Background

          Upper limb associated reactions (ARs) are common in people with acquired brain injury (ABI). Despite this, there is no gold-standard outcome measure and no kinematic description of this movement disorder. The aim of this study was to determine the upper limb kinematic variables most frequently affected by ARs in people with ABI compared with a healthy cohort at matched walking speed intention.

          Methods

          A convenience sample of 36 healthy control adults (HCs) and 42 people with ABI who had upper limb ARs during walking were recruited and underwent assessment of their self-selected walking speed using the criterion-reference three dimensional motion analysis (3DMA) at Epworth Hospital, Melbourne. Shoulder flexion, abduction and rotation, elbow flexion, forearm rotation and wrist flexion were assessed. The mean angle, standard deviation (SD), peak joint angles and total joint angle range of motion (ROM) were calculated for each axis across the gait cycle. On a group level, ANCOVA was used to assess the between-group differences for each upper limb kinematic outcome variable. To quantify abnormality prevalence on an individual participant level, the percentage of ABI participants that were outside of the 95% confidence interval of the HC sample for each variable were calculated.

          Results

          There were significant between-group differences for all elbow and shoulder abduction outcome variables ( p < 0.01), most shoulder flexion variables (except for shoulder extension peak), forearm rotation SD and ROM and for wrist flexion ROM. Elbow flexion and shoulder abduction were the axes most frequently affected by ARs. Despite the elbow being the most prevalently affected (38/42, 90%), a large proportion of participants had abnormality, defined as ±1.96 SD of the HC mean, present at the shoulder (32/42, 76%), forearm (20/42, 48%) and wrist joints (10/42, 24%).

          Conclusion

          This study provides valuable information on ARs, and highlights the need for clinical assessment of ARs to include all of the major joints of the upper limb. This may inform the development of a criterion-reference outcome measure or classification system specific to ARs.

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          Most cited references39

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          Two simple methods for determining gait events during treadmill and overground walking using kinematic data.

          The determination of gait events such as heel strike and toe-off provide the basis for defining stance and swing phases of gait cycles. Two algorithms for determining event times for treadmill and overground walking based solely on kinematic data are presented. Kinematic data from treadmill walking trials lasting 20-45s were collected from three subject populations (healthy young, n=7; multiple sclerosis, n=7; stroke, n=4). Overground walking trials consisted of approximately eight successful passes over two force plates for a healthy subject population (n=5). Time of heel strike and toe-off were determined using the two new computational techniques and compared to events detected using vertical ground reaction force (GRF) as a gold standard. The two algorithms determined 94% of the treadmill events from healthy subjects within one frame (0.0167s) of the GRF events. In the impaired populations, 89% of treadmill events were within two frames (0.0334s) of the GRF events. For overground trials, 98% of events were within two frames. Automatic event detection from the two kinematic-based algorithms will aid researchers by accurately determining gait events during the analysis of treadmill and overground walking.
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            The Gait Deviation Index: a new comprehensive index of gait pathology.

            This article describes a new multivariate measure of overall gait pathology called the Gait Deviation Index (GDI). The first step in developing the GDI was to use kinematic data from a large number of walking strides to derive a set of mutually independent joint rotation patterns that efficiently describe gait. These patterns are called gait features. Linear combinations of the first 15 gait features produced a 98% faithful reconstruction of both the data from which they were derived and 1000 validation strides not used in the derivation. The GDI was then defined as a scaled distance between the 15 gait feature scores for a subject and the average of the same 15 gait feature scores for a control group of typically developing (TD) children. Concurrent and face validity data for the GDI are presented through comparisons with the Gillette Gait Index (GGI), Gillette Functional Assessment Questionnaire Walking Scale (FAQ), and topographic classifications within the diagnosis of Cerebral Palsy (CP). The GDI and GGI are strongly correlated (r(2)=0.56). The GDI scales with FAQ level, distinguishes levels from one another, and is normally distributed across FAQ levels six to ten and among TD children. The GDI also scales with respect to clinical involvement based on topographic CP classification in Hemiplegia Types I-IV, Diplegia, Triplegia and Quadriplegia. The GDI offers an alternative to the GGI as a comprehensive quantitative gait pathology index, and can be readily computed using the electronic addendum provided with this article.
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              The gait profile score and movement analysis profile.

              The Gait Deviation Index (GDI) has been proposed as an index of overall gait pathology. This study proposes an interpretation of the difference measure upon which the GDI is based, which naturally leads to the definition of a similar index, the Gait Profile Score (GPS). The GPS can be calculated independently of the feature analysis upon which the GDI is based. Understanding what the underlying difference measure represents also suggests that reporting a raw score, as the GPS does, may have advantages over the logarithmic transformation and z-scaling incorporated in the GDI. It also leads to the concept of a Movement Analysis Profile (MAP) to summarise much of the information contained within kinematic data. A validation study on all children attending a paediatric gait analysis service over 3 years (407 children) provides evidence to support the use of the GPS through analysis of its frequency distribution across different Gross Motor Function Classification System (GMFCS) and Gillette Functional Assessment Questionnaire (FAQ) categories, investigation of intra-session variability, and correlation with the square root of GGI. Correlation with GDI confirms the strong relationship between the two measures. The study concludes that GDI and GPS are alternative and closely related measures. The GDI has prior art and is particularly useful in applications arising out of feature analysis such as cluster analysis or subject matching. The GPS will be easier to calculate for new models where a large reference dataset is not available and in association with applications using the MAP.
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                Author and article information

                Contributors
                Michelle.Kahn@epworth.org.au
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central (London )
                1743-0003
                27 December 2019
                27 December 2019
                2019
                : 16
                : 160
                Affiliations
                [1 ]ISNI 0000 0001 0459 5396, GRID grid.414539.e, Department of Physiotherapy, , Epworth Rehabilitation, Epworth Healthcare, ; Melbourne, Australia
                [2 ]ISNI 0000 0001 1555 3415, GRID grid.1034.6, School of Health and Sport Sciences, , University of Sunshine Coast, ; Sunshine Coast, Australia
                [3 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, School of Physiotherapy, , The University of Melbourne, ; Melbourne, Australia
                [4 ]Epworth Monash Rehabilitation Unit (EMReM), Melbourne, Australia
                [5 ]ISNI 0000 0001 2342 0938, GRID grid.1018.8, La Trobe Sport and Exercise Medicine Research Centre, , La Trobe University, ; Bundoora, Australia
                Author information
                http://orcid.org/0000-0003-3935-110X
                Article
                637
                10.1186/s12984-019-0637-2
                6935151
                31881975
                c54bec25-db30-42c2-9fee-a80ad37c46b7
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 26 August 2019
                : 13 December 2019
                Funding
                Funded by: Royal Automobile Club of Victoria (RACV)
                Funded by: FundRef http://dx.doi.org/10.13039/501100008305, Physiotherapy Research Foundation;
                Award ID: T15-APA001
                Award Recipient :
                Funded by: Epworth Research Institute (AU)
                Funded by: National Health and Medical Research Council R.D. Wright Biomedical Fellowship
                Award ID: 1090415
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Neurosciences
                acquired brain injury,upper limb,associated reactions,three-dimensional motion analysis,kinematics

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