Trinler Ursula, Hollands Kristen, Jones Richard, Baker Richard
University of Salford, School of Health Science, Allerton Building, Frederick Road Campus, Salford, M6 6PU, United Kingdom; BG Unfallklinik Ludwigshafen, Zentrum für Bewegungsanalytik, Forschung und Lehre, Ludwig-Guttmann Straße 13, 67071 Ludwigshafen, Germany.
University of Salford, School of Health Science, Allerton Building, Frederick Road Campus, Salford, M6 6PU, United Kingdom.
Gait Posture. 2018 Mar;61:353-361. doi: 10.1016/j.gaitpost.2018.02.005. Epub 2018 Feb 6.
Computational methods to estimate muscle forces during walking are becoming more common in biomechanical research but not yet in clinical gait analysis. This systematic review aims to identify the current state-of-the-art, examine the differences between approaches, and consider applicability of the current approaches in clinical gait analysis. A systematic database search identified studies including estimated muscle force profiles of the lower limb during healthy walking. These were rated for quality and the muscle force profiles digitised for comparison. From 13.449 identified studies, 22 were finally included which used four modelling approaches: static optimisation, enhanced static optimisation, forward dynamics and EMG-driven. These used a range of different musculoskeletal models, muscle-tendon characteristics and cost functions. There is visually broad agreement between and within approaches about when muscles are active throughout the gait cycle. There remain, considerable differences (CV 7%-151%, range of timing of peak forces in gait cycle 1%-31%) in patterns and magnitudes of force between and within modelling approaches. The main source of this variability is not clear. Different musculoskeletal models, experimental protocols, and modelling approaches will clearly have an effect as will the variability of joint kinetics between healthy individuals. Limited validation of modelling approaches, particularly at the level of individual participants, makes it difficult to conclude if any of the approaches give consistently better estimates than others. While muscle force modelling has clear potential to enhance clinical gait analyses future research is needed to improve validation, accuracy and feasibility of implementation in clinical practice.
在生物力学研究中,用于估计步行过程中肌肉力量的计算方法正变得越来越普遍,但在临床步态分析中尚未如此。本系统综述旨在确定当前的技术水平,研究不同方法之间的差异,并考虑当前方法在临床步态分析中的适用性。通过系统的数据库搜索,确定了包括健康步行过程中下肢估计肌肉力量曲线的研究。对这些研究进行质量评级,并将肌肉力量曲线数字化以进行比较。在13449项已识别的研究中,最终纳入了22项研究,这些研究使用了四种建模方法:静态优化、增强静态优化、正向动力学和肌电图驱动。这些研究使用了一系列不同的肌肉骨骼模型、肌肉肌腱特征和代价函数。在整个步态周期中肌肉何时活跃方面,不同方法之间以及同一方法内部在视觉上有广泛的一致性。在建模方法之间以及同一建模方法内部,力量的模式和大小仍存在相当大的差异(变异系数7%-151%,步态周期中峰值力量的时间范围1%-31%)。这种变异性的主要来源尚不清楚。不同的肌肉骨骼模型、实验方案和建模方法显然会产生影响,健康个体之间关节动力学的变异性也会如此。建模方法的验证有限,尤其是在个体参与者层面,这使得难以得出是否有任何一种方法能始终比其他方法给出更好估计的结论。虽然肌肉力量建模显然有潜力增强临床步态分析,但未来仍需要开展研究,以提高其在临床实践中的验证、准确性和实施可行性。