Electromyography as an Important Parameter for a Proper Assessment of Dynamic Muscles Strength in Gait Analysis

E. Ravera, P. Catalfamo, M. Crespo, A. Braidot
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引用次数: 6

Abstract

Normal gait is a functional movement involving the most efficient energy transfer. Cerebral Palsy describes a group of developmental d isorders of movement and posture causing activity restriction or d isability. It represents one of the most common cause of physical disability in ch ildren. Gait analysis provides great contributions to the understanding of gait disorders originating fro m musculoskeletal abnormalit ies. In these patients provides a mean for a mo re co mprehensive treatment plan. At this point, the potential and necessity of using correct biomechanical models that consistently study the abnormalities beco me clear. Reinforcing and correcting a simp le gait analysis and eliminating the unknowns when selecting the appropriate treatment are crucial in clin ical settings. In this paper a musculoskeletal th igh model with a simple rescaling method using subject-specific anthropometric data is presented. The thigh model estimates muscle forces of the six muscles through the walking based in static optimization without including electromyography data as input. We used the model proposed for identify the muscles strength involved in normal and pathological gait. The forces obtaining for normal subjects achieved high concordance with their electro myography data and outcomes to other musculoskeletal model recently presented. In other hand, in patients with cerebral palsy with crouch gait the outcomes of our musculoskeletal model don't show a correct concordance with their electro myography data. For this reason we get on the conclusion that take electro myography data became important in modeling a pathological gait as crouch gait.
肌电图是步态分析中正确评估动态肌肉力量的重要参数
正常的步态是一种涉及最有效的能量转移的功能性运动。脑瘫描述了一组导致活动受限或残疾的运动和姿势发育障碍。它是儿童身体残疾的最常见原因之一。步态分析为理解源自肌肉骨骼异常的步态障碍提供了巨大的贡献。为这些患者提供了一种更为全面的治疗方案。在这一点上,使用正确的生物力学模型来持续研究异常的潜力和必要性变得清晰起来。加强和纠正简单的步态分析,并在选择适当的治疗时消除未知因素,在临床环境中至关重要。本文提出了一种基于人体测量数据的肌肉骨骼高度模型,该模型具有简单的缩放方法。大腿模型在静态优化的基础上,通过步行来估计六块肌肉的肌力,而不包括肌电图数据作为输入。我们使用所提出的模型来识别正常和病理步态所涉及的肌肉力量。正常受试者获得的力与他们的肌电图数据和最近提出的其他肌肉骨骼模型的结果高度一致。另一方面,在患有蹲姿的脑瘫患者中,我们的肌肉骨骼模型的结果与他们的肌电图数据不一致。因此我们得出结论,肌电图数据在蹲伏步态等病理步态建模中具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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