Prentice S D, Patla A E, Stacey D A
Gait and Posture Laboratory, Department of Kinesiology, University of Waterloo, Waterloo N2L 3G1, Canada.
J Electromyogr Kinesiol. 2001 Feb;11(1):19-30. doi: 10.1016/s1050-6411(00)00038-9.
Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.
熟练的运动行为需要来自中枢神经系统（CNS）内不同层次的信息。数学模型使研究人员能够模拟各种机制，以便理解运动控制系统的组织。虽然很难充分描述运动控制系统的众多输入，但一种替代策略可能是使用运动学运动计划来表示基于中枢神经系统可能在运动学水平上计划运动的可能性的运动控制系统的复杂输入。我们建议使用人工神经网络（ANN）模型来表示运动学计划到必要运动模式的转换。本质上，实际肢体运动的运动学表示被用作ANN模型的输入，该模型生成下肢和躯干8块肌肉的肌电图活动。来自各种步态条件的数据对于开发一个能够适应日常活动中遇到的各种环境条件的强大模型是必要的。共有120个步行步幅，代表正常行走以及正常步态在步频、步长、站立宽度或所需足部间隙方面被修改的十种情况。最终网络根据其在个体步行试验中预测肌电图活动的能力以及其表示特定步态条件的一般激活模式的能力进行评估。预测的肌电图模式与实验记录的模式紧密匹配，显示出每种修改所需的适当幅度和时间相位。96种肌肉/步态条件中只有2种的均方根误差高于0.10，只有5种肌肉/步态条件的相关性低于0.80（大多数高于0.90），并且只有25种肌肉/步态条件在超过25%的步态周期内偏离肌肉活动的正常范围。这些结果表明单网络人工神经网络能够表示运动学运动计划与正常稳态运动所需的必要肌肉激活之间的转换，但它们也能够为需要改变步行速度、足部放置和足部间隙的情况生成肌肉激活模式。这种类型网络的能力对运动控制的基本理解和康复医学中使用的人工控制系统的实际实现都有影响。
