Arjunan Sridhar P, Siddiqi Ariba, Swaminathan Ramakrishnan, Kumar Dinesh K
SRM Institute of Science and Technology, Chennai, India.
Biosignals Lab, School of Engineering, RMIT University, Melbourne, VIC, Australia.
Proc Inst Mech Eng H. 2020 Feb;234(2):200-209. doi: 10.1177/0954411919890150. Epub 2019 Nov 27.
This study reports a surface electromyogram and force of contraction model. The objective was to investigate the effect of changes in the size, type and number of motor units in the Tibialis Anterior muscle to surface electromyogram and force of dorsiflexion. A computational model to simulate surface electromyogram and associated force of contraction by the Tibialis Anterior muscle was developed. This model was simulated for isometric dorsiflexion, and comparative experiments were conducted for validation. Repeated simulations were performed to investigate the different parameters and evaluate inter-experimental variability. An equivalence statistical test and the Bland-Altman method were used to observe the significance between the simulated and experimental data. Simulated and experimentally recorded data had high similarity for the three measures: maximal power of power spectral density ( < 0.0001), root mean square of surface electromyogram ( < 0.0001) and force recorded at the footplate ( < 0.03). Inter-subject variability in the experimental results was in-line with the variability in the repeated simulation results. This experimentally validated computational model for the surface electromyogram and force of the Tibialis Anterior muscle is significant as it allows the examination of three important muscular factors associated with ageing and disease: size, fibre type and number of motor units.
本研究报告了一种表面肌电图和收缩力模型。目的是研究胫骨前肌运动单位的大小、类型和数量变化对表面肌电图和背屈力的影响。开发了一种计算模型，用于模拟胫骨前肌的表面肌电图和相关收缩力。该模型针对等长背屈进行模拟，并进行了对比实验以进行验证。进行了重复模拟以研究不同参数并评估实验间的变异性。使用等效性统计检验和布兰德-奥特曼方法来观察模拟数据和实验数据之间的显著性。模拟数据和实验记录数据在以下三个测量指标上具有高度相似性：功率谱密度的最大功率（<0.0001）、表面肌电图的均方根（<0.0001）以及在脚板处记录的力（<0.03）。实验结果中的个体间变异性与重复模拟结果中的变异性一致。这种经过实验验证的胫骨前肌表面肌电图和力的计算模型具有重要意义，因为它允许研究与衰老和疾病相关的三个重要肌肉因素：大小、纤维类型和运动单位数量。
