Department of Electrical and Electronic Engineering, Imperial College London London, UK.
Department of Electrical and Electronic Engineering, Imperial College London London, UK ; Center for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London London, UK.
Front Neurosci. 2014 Jun 25;8:181. doi: 10.3389/fnins.2014.00181. eCollection 2014.
Accurate models of proprioceptive neural patterns could 1 day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimization) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modeling. This paper uses and proposes a number of approximations and optimizations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed.
精确的本体感觉神经模式模型有朝一日可能在为截肢者创造直观的本体感觉神经假体方面发挥重要作用。本文探讨了将生物力学和本体感受器模型的有效实现结合起来，以生成模拟人体肌肉本体感觉模式的信号，用于假体反馈的未来实验工作。提出了一个具有 7 个自由度和 17 个肌肉的上肢神经肌肉骨骼模型，并实时生成肌梭和高尔基腱器官神经放电模式的估计。与以前的神经肌肉骨骼模型不同，在这种应用中，肌肉激活和兴奋水平是未知的，因此集成了逆动力学工具（静态优化）来估计这些变量。本体感觉假体需要便携，这与标准生物力学和本体感受器建模的计算密集性质不兼容。本文使用并提出了一些近似和优化方法，使实时操作在便携硬件上成为可能。最后，确定并讨论了模拟直观本体感觉假体的自然反馈的技术障碍，以及现有模型的问题和局限性。
