Zhang Qin, Hayashibe Mitsuhiro, Papaiordanidou Maria, Fraisse Philippe, Fattal Charles, Guiraud David
INRIA Sophia-Antipolis, DEMAR project, LIRMM, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3523-6. doi: 10.1109/IEMBS.2010.5627745.
Muscle fatigue is an unavoidable problem when electrical stimulation is applied to paralyzed muscles. The detection and compensation of muscle fatigue is essential to avoid movement failure and achieve desired trajectory. This work aims to predict ankle plantar-flexion torque using stimulus evoked EMG (eEMG) during different muscle fatigue states. Five spinal cord injured patients were recruited for this study. An intermittent fatigue protocol was delivered to triceps surae muscle to induce muscle fatigue. A hammerstein model was used to capture the muscle contraction dynamics to represent eEMG-torque relationship. The prediction of ankle torque was based on measured eEMG and past measured or past predicted torque. The latter approach makes it possible to use eEMG as a synthetic force sensor when force measurement is not available in daily use. Some previous researches suggested to use eEMG information directly to detect and predict muscle force during fatigue assuming a fixed relationship between eEMG and generated force. However, we found that the prediction became less precise with the increase of muscle fatigue when fixed parameter model was used. Therefore, we carried out the torque prediction with an adaptive parameters using the latest measurement. The prediction of adapted model was improved with 16.7%-50.8% comparing to the fixed model.
当对瘫痪肌肉施加电刺激时，肌肉疲劳是一个不可避免的问题。检测和补偿肌肉疲劳对于避免运动失败和实现期望轨迹至关重要。这项工作旨在利用不同肌肉疲劳状态下的刺激诱发肌电图（eEMG）来预测踝关节跖屈扭矩。本研究招募了5名脊髓损伤患者。对小腿三头肌实施间歇性疲劳方案以诱发肌肉疲劳。使用哈默斯坦模型来捕捉肌肉收缩动态，以表征eEMG与扭矩的关系。踝关节扭矩的预测基于测量的eEMG以及过去测量或过去预测的扭矩。后一种方法使得在日常使用中无法进行力测量时，能够将eEMG用作合成力传感器。一些先前的研究建议在假设eEMG与产生的力之间存在固定关系的情况下，直接使用eEMG信息来检测和预测疲劳期间的肌肉力量。然而，我们发现当使用固定参数模型时，随着肌肉疲劳的增加，预测变得不那么精确。因此，我们使用最新测量值进行了具有自适应参数的扭矩预测。与固定模型相比，自适应模型的预测提高了16.7% - 50.8%。
