Cai Zhijun, Bai Er-Wei, Shields Richard K
Dept of Elect rical and Computer Engineering, University of Iowa, Iowa City, IA 52242.
Biomed Signal Process Control. 2010 Apr;5(2):87-93. doi: 10.1016/j.bspc.2009.12.001.
Electrical muscle stimulation demonstrates potential for preventing muscle atrophy and for restoring functional movement after spinal cord injury (SCI). Control systems used to optimize delivery of electrical stimulation protocols depend upon the algorithms generated using computational models of paralyzed muscle force output. The Hill-Huxley-type model, while being highly accurate, is also very complex, making it difficult for real-time implementation. In this paper, we propose a Wiener-Hammerstein system to model the paralyzed skeletal muscle under electrical stimulus conditions. The proposed model has substantial advantages in identification algorithm analysis and implementation including computational complexity and convergence, which enable it to be used in real-time model implementation. Experimental data sets from the soleus muscles of fourteen subjects with SCI were collected and tested. The simulation results show that the proposed model outperforms the Hill-Huxley-type model not only in peak force prediction, but also in fitting performance for force output of each individual stimulation train.
电肌肉刺激显示出预防脊髓损伤（SCI）后肌肉萎缩和恢复功能运动的潜力。用于优化电刺激方案输送的控制系统依赖于使用瘫痪肌肉力输出计算模型生成的算法。希尔 - 赫胥黎型模型虽然高度精确，但也非常复杂，难以实时实现。在本文中，我们提出了一种维纳 - 哈默斯坦系统来模拟电刺激条件下的瘫痪骨骼肌。所提出的模型在识别算法分析和实现方面具有显著优势，包括计算复杂度和收敛性，这使其能够用于实时模型实现。收集并测试了来自14名脊髓损伤受试者比目鱼肌的实验数据集。仿真结果表明，所提出的模型不仅在峰值力预测方面优于希尔 - 赫胥黎型模型，而且在每个单独刺激序列的力输出拟合性能方面也更优。
