Dzulkifli Muhammad Afiq, Hamzaid Nur Azah, Davis Glen M, Hasnan Nazirah
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Discipline of Exercise and Sports Sciences, Faculty of Health Sciences, The University of Sydney, Sydney, NSW, Australia.
Front Neurorobot. 2018 Aug 10;12:50. doi: 10.3389/fnbot.2018.00050. eCollection 2018.
This study sought to design and deploy a torque monitoring system using an artificial neural network (ANN) with mechanomyography (MMG) for situations where muscle torque cannot be independently quantified. The MMG signals from the quadriceps were used to derive knee torque during prolonged functional electrical stimulation (FES)-assisted isometric knee extensions and during standing in spinal cord injured (SCI) individuals. Three individuals with motor-complete SCI performed FES-evoked isometric quadriceps contractions on a Biodex dynamometer at 30° knee angle and at a fixed stimulation current, until the torque had declined to a minimum required for ANN model development. Two ANN models were developed based on different inputs; Root mean square (RMS) MMG and RMS-Zero crossing (ZC) which were derived from MMG. The performance of the ANN was evaluated by comparing model predicted torque against the actual torque derived from the dynamometer. MMG data from 5 other individuals with SCI who performed FES-evoked standing to fatigue-failure were used to validate the RMS and RMS-ZC ANN models. RMS and RMS-ZC of the MMG obtained from the FES standing experiments were then provided as inputs to the developed ANN models to calculate the predicted torque during the FES-evoked standing. The average correlation between the knee extension-predicted torque and the actual torque outputs were 0.87 ± 0.11 for RMS and 0.84 ± 0.13 for RMS-ZC. The average accuracy was 79 ± 14% for RMS and 86 ± 11% for RMS-ZC. The two models revealed significant trends in torque decrease, both suggesting a critical point around 50% torque drop where there were significant changes observed in RMS and RMS-ZC patterns. Based on these findings, both RMS and RMS-ZC ANN models performed similarly well in predicting FES-evoked knee extension torques in this population. However, interference was observed in the RMS-ZC values at a time around knee buckling. The developed ANN models could be used to estimate muscle torque in real-time, thereby providing safer automated FES control of standing in persons with motor-complete SCI.
本研究旨在设计并部署一种扭矩监测系统，该系统使用带有肌动电流图（MMG）的人工神经网络（ANN），用于无法独立量化肌肉扭矩的情况。在长期功能性电刺激（FES）辅助的等长膝关节伸展过程中以及脊髓损伤（SCI）个体站立期间，来自股四头肌的MMG信号被用于推导膝关节扭矩。三名运动完全性SCI个体在Biodex测力计上以30°膝关节角度和固定刺激电流进行FES诱发的等长股四头肌收缩，直到扭矩下降到ANN模型开发所需的最小值。基于不同输入开发了两个ANN模型；从MMG导出的均方根（RMS）MMG和均方根过零（ZC）。通过将模型预测扭矩与测力计得出的实际扭矩进行比较来评估ANN的性能。来自其他5名进行FES诱发站立直至疲劳衰竭的SCI个体的MMG数据用于验证RMS和RMS-ZC ANN模型。然后将从FES站立实验中获得的MMG的RMS和RMS-ZC作为输入提供给已开发的ANN模型，以计算FES诱发站立期间的预测扭矩。膝关节伸展预测扭矩与实际扭矩输出之间的平均相关性，RMS为0.87±0.11，RMS-ZC为0.84±0.13。RMS的平均准确率为79±14%，RMS-ZC为86±11%。这两个模型都显示出扭矩下降的显著趋势，均表明在扭矩下降约50%处存在一个临界点，此时在RMS和RMS-ZC模式中观察到显著变化。基于这些发现，RMS和RMS-ZC ANN模型在预测该人群中FES诱发的膝关节伸展扭矩方面表现同样出色。然而，在膝关节屈曲前后的某个时间点，RMS-ZC值出现了干扰。所开发的ANN模型可用于实时估计肌肉扭矩，从而为运动完全性SCI患者的站立提供更安全的自动FES控制。
