Trinler Ursula, Leboeuf Fabien, 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 Jul;64:266-273. doi: 10.1016/j.gaitpost.2018.06.115. Epub 2018 Jun 19.
Muscle force estimation could improve clinical gait analysis by enhancing insight into causes of impairments and informing targeted treatments. However, it is not currently standard practice to use muscle force models to augment clinical gait analysis, partly, because robust validations of estimated muscle activations, underpinning force modelling processes, against recorded electromyography (EMG) are lacking.
Therefore, in order to facilitate future clinical use, this study sought to validate estimated lower limb muscle activation using two mathematical models (static optimisation SO, computed muscle control CMC) against recorded muscle activations of ten healthy participants.
Participants walked at five speeds. Visual agreement in activation onset and offset as well as linear correlation (r) and mean absolute error (MAE) between models and EMG were evaluated.
MAE between measured and recorded activations were variable across speeds (SO vs EMG 15-68%, CMC vs EMG 13-69%). Slower speeds resulted in smaller deviations (mean MAE < 30%) than faster speeds. Correlation was high (r > 0.5) for only 11/40 (CMC) and 6/40 (SO) conditions (muscles X speeds) compared to EMG.
Modelling approaches do not yet show sufficient consistency of agreement between estimated and recorded muscle activation to support recommending immediate clinical adoption of muscle force modelling. This may be because assumptions underlying muscle activation estimations (e.g. muscles' anatomy and maximum voluntary contraction) are not yet sufficiently individualizable. Future research needs to find timely and cost efficient ways to scale musculoskeletal models for better individualisation to facilitate future clinical implementation.
肌肉力量估计可以通过增强对损伤原因的洞察并为靶向治疗提供信息，从而改善临床步态分析。然而，目前使用肌肉力量模型来增强临床步态分析并非标准做法，部分原因是缺乏针对记录的肌电图（EMG）对支撑力量建模过程的估计肌肉激活进行的有力验证。
因此，为了便于未来的临床应用，本研究试图使用两种数学模型（静态优化SO、计算肌肉控制CMC）针对十名健康参与者记录的肌肉激活来验证估计的下肢肌肉激活。
参与者以五种速度行走。评估模型与肌电图之间在激活起始和偏移方面的视觉一致性以及线性相关性（r）和平均绝对误差（MAE）。
测量和记录的激活之间的MAE在不同速度下有所不同（SO与EMG相比为15 - 68%，CMC与EMG相比为13 - 69%）。较慢的速度导致的偏差比较快的速度小（平均MAE < 30%）。与肌电图相比，仅11/40（CMC）和6/40（SO）条件（肌肉X速度）的相关性较高（r > 0.5）。
建模方法在估计和记录的肌肉激活之间尚未显示出足够的一致性，以支持推荐立即在临床中采用肌肉力量建模。这可能是因为肌肉激活估计所依据的假设（例如肌肉的解剖结构和最大自主收缩）尚未充分个体化。未来的研究需要找到及时且经济高效的方法来缩放肌肉骨骼模型，以实现更好的个体化，从而促进未来的临床应用。
