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      Sit-to-walk strategy classification in healthy adults using hip and knee joint angles at gait initiation

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          Abstract

          Forward continuation, balance, and sit-to-stand-and-walk (STSW) are three common movement strategies during sit-to-walk (STW) executions. Literature identifies these strategies through biomechanical parameters using gold standard laboratory equipment, which is expensive, bulky, and requires significant post-processing. STW strategy becomes apparent at gait-initiation (GI) and the hip/knee are primary contributors in STW, therefore, this study proposes to use the hip/knee joint angles at GI as an alternate method of strategy classification. To achieve this, K-means clustering was implemented using three clusters corresponding to the three STW strategies; and two feature sets corresponding to the hip/knee angles (derived from motion capture data); from an open access online database (age: 21–80 years; n = 10). The results identified forward continuation with the lowest hip/knee extension, followed by balance and then STSW, at GI. Using this classification, strategy biomechanics were investigated by deriving the established biomechanical quantities from literature. The biomechanical parameters that significantly varied between strategies ( P < 0.05) were time, horizontal centre of mass (COM) momentum, braking impulse, centre of pressure (COP) range and velocities, COP–COM separation, hip/knee torque and movement fluency. This alternate method of strategy classification forms a generalized framework for describing STW executions and is consistent with literature, thus validating the joint angle classification method.

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          Most cited references26

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          OpenSim: open-source software to create and analyze dynamic simulations of movement.

          Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.
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            OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement

            Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.
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              Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study.

              Falls in elderly people are a major health burden, especially in the long-term care environment. Yet little objective evidence is available for how and why falls occur in this population. We aimed to provide such evidence by analysing real-life falls in long-term care captured on video. We did this observational study between April 20, 2007, and June 23, 2010, in two long-term care facilities in British Columbia, Canada. Digital video cameras were installed in common areas (dining rooms, lounges, hallways). When a fall occurred, facility staff completed an incident report and contacted our teams so that we could collect video footage. A team reviewed each fall video with a validated questionnaire that probed the cause of imbalance and activity at the time of falling. We then tested whether differences existed in the proportion of participants falling due to the various causes, and while engaging in various activities, with generalised linear models, repeated measures logistic regression, and log-linear Poisson regression. We captured 227 falls from 130 individuals (mean age 78 years, SD 10). The most frequent cause of falling was incorrect weight shifting, which accounted for 41% (93 of 227) of falls, followed by trip or stumble (48, 21%), hit or bump (25, 11%), loss of support (25, 11%), and collapse (24, 11%). Slipping accounted for only 3% (six) of falls. The three activities associated with the highest proportion of falls were forward walking (54 of 227 falls, 24%), standing quietly (29 falls, 13%), and sitting down (28 falls, 12%). Compared with previous reports from the long-term care setting, we identified a higher occurrence of falls during standing and transferring, a lower occurrence during walking, and a larger proportion due to centre-of-mass perturbations than base-of-support perturbations. By providing insight into the sequences of events that most commonly lead to falls, our results should lead to more valid and effective approaches for balance assessment and fall prevention in long-term care. Canadian Institutes for Health Research. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                alpha.agape@monash.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 October 2023
                3 October 2023
                2023
                : 13
                : 16640
                Affiliations
                [1 ]GRID grid.440425.3, ISNI 0000 0004 1798 0746, School of Engineering, , Monash University, ; Subang Jaya, Selangor Malaysia
                [2 ]Malaysian Research Institute on Ageing, Universiti Putra Malaysia, ( https://ror.org/02e91jd64) Serdang, Selangor Malaysia
                Article
                43148
                10.1038/s41598-023-43148-0
                10547676
                37789077
                d01c0f5b-85c7-47f5-a1a8-824c222af4f2
                © 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
                : 21 March 2023
                : 20 September 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003093, Ministry of Higher Education, Malaysia;
                Award ID: FRGS/1/2022/TK07/MUSM/02/2
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                biomedical engineering,musculoskeletal system,quality of life
                Uncategorized
                biomedical engineering, musculoskeletal system, quality of life

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