Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, and Advanced Design and Prototyping Technologies Institute, Griffith University, QLD, 4222, Southport, Australia.
Department of Orthopaedic Surgery, Children's Health Queensland Hospital and Health Service, Brisbane, QLD, 4101, Australia.
Sci Rep. 2022 Mar 4;12(1):3599. doi: 10.1038/s41598-022-07541-5.
Preparing children with cerebral palsy prior to gait analysis may be a challenging and time-intensive task, especially when large number of sensors are involved. Collecting minimum number of electromyograms (EMG) and yet providing adequate information for clinical assessment might improve clinical workflow. The main goal of this study was to develop a method to estimate activation patterns of lower limb muscles from EMG measured from a small set of muscles in children with cerebral palsy. We developed and implemented a muscle synergy extrapolation method able to estimate the full set of lower limbs muscle activation patterns from only three experimentally measured EMG. Specifically, we extracted a set of hybrid muscle synergies from muscle activation patterns of children with cerebral palsy and their healthy counterparts. Next, those muscle synergies were used to estimate activation patterns of muscles, which were not initially measured in children with cerebral palsy. Two best combinations with three (medial gastrocnemius, semi membranous, and vastus lateralis) and four (lateral gastrocnemius, semi membranous, sartorius, and vastus medialis) experimental EMG were able to estimate the full set of 10 muscle activation patterns with mean (± standard deviation) variance accounted for of 79.93 (± 9.64)% and 79.15 (± 6.40)%, respectively, using only three muscle synergies. In conclusion, muscle activation patterns of unmeasured muscles in children with cerebral palsy can be estimated from EMG measured from three to four muscles using our muscle synergy extrapolation method. In the future, the proposed muscle synergy-based method could be employed in gait clinics to minimise the required preparation time.
为脑瘫儿童进行步态分析前的准备工作可能具有挑战性且耗时，尤其是当涉及大量传感器时。采集尽可能少的肌电图（EMG），同时为临床评估提供足够的信息，可能会改善临床工作流程。本研究的主要目的是开发一种从脑瘫儿童少量肌肉测量的 EMG 中估计下肢肌肉激活模式的方法。我们开发并实施了一种肌肉协同作用外推方法，能够仅从三个实验测量的 EMG 中估计整个下肢肌肉的激活模式。具体来说，我们从脑瘫儿童及其健康对照者的肌肉激活模式中提取了一组混合肌肉协同作用。然后，使用这些肌肉协同作用来估计最初未在脑瘫儿童中测量的肌肉的激活模式。用三个（腓肠肌内侧、半膜肌和股外侧肌）和四个（腓肠肌外侧、半膜肌、缝匠肌和股内侧肌）实验性 EMG 提取的两组最佳组合可以分别以 79.93%（±9.64%）和 79.15%（±6.40%）的平均方差来估计 10 个肌肉激活模式中的全部，而使用的肌肉协同作用只有三个。总之，使用我们的肌肉协同作用外推方法，可以从测量的三个到四个肌肉的 EMG 中估计脑瘫儿童未测量肌肉的肌肉激活模式。将来，所提出的基于肌肉协同作用的方法可以在步态诊所中使用，以最大限度地减少所需的准备时间。
