Artificial Neural Network modelling for studying posture

用于研究姿势的人工神经网络建模

• 批准号: 6400894
• 负责人: GE WU
• 金额: $ 7.55万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2003-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6400894
• 关键词: artificial intelligence; balance; mathematical model; model design /development; posture; psychomotor function; somesthetic sensory cortex; vestibular pathway; visual pathways

A major challenge with the application of artificial neural network (ANN) modeling in examining the motor-sensory relation in postural control is that the neural synaptic weights that relate the inputs to the outputs of the ANN are chaotic in nature. In an earlier study through theoretical analysis, numerical simulations and experimental tests, the PI and her colleague have found that these weights are interdependent. The product of these weights is a statistically stable variable and can be used to quantify the input-output relation of the ANN, called Q value. The objective of this proposed research is to extend the above work to the area of human postural control. Specifically, we will explore whether or not a Q value concept in an ANN can be used to quantify the motor-sensory relation in a classical postural control task - maintaining upright balance when the supporting base is suddenly rotated in a toes up direction. We will construct an ANN model that includes two outputs and seven inputs. The two outputs are the EMG signals from ankle dorsiflexor and plantartlexor in response to the onset of the supporting base rotation. The seven inputs are average eye-target distance (distance from eye center to a visual target), head acceleration (both linear and angular), ankle joint rotation, ankle joint rotation speed, and ground reaction forces (both normal and shear) under feet. These inputs represent the mechanical stimulation to the visual, vestibular, and somatosensory systems, respectively. These inputs and outputs variables will be measured directly from two groups of elderly subjects: peripherally neuropathic and normal, non-peripherally neuropathic. We will then determine the weights in the ANN model by a backward-propagation training routine, and the corresponding Q values relating each output to each of the inputs. We will statistically compare the Q values among the multiple sensory inputs within each group. We hypothesize that under this experimental condition, the Q values relating postural muscle activities to the somatosensory inputs would be: (1) significantly higher than the Q values relating to other sensory inputs (such as visual and vestibular inputs) in normal, non-neuropathic subjects; and (2) significantly lower than the Q values relating to other sensory inputs in neuropathic subjects. It is hoped that this study will contribute to our understanding of how sensory information is used to control postural muscle activities, and how a modification in the motor-sensory relation can result in increased postural stability or falls.

人工神经网络（ANN）应用的一个主要挑战 在检查姿势控制中的运动-感觉关系时， 将ANN的输入与输出相关联的神经突触权重是混沌的 在自然界中。在早期的研究中，通过理论分析、数值模拟 和实验测试，PI和她的同事发现，这些重量是 相互依存这些权重的乘积是统计稳定变量， 可以用来量化人工神经网络的输入输出关系，称为Q值。的 这项研究的目的是将上述工作扩展到以下领域： 人体姿势控制具体来说，我们将探讨Q值是否 人工神经网络中的概念可以用来量化经典运动中的运动-感觉关系。 姿势控制任务-当支撑底座 突然朝脚趾向上的方向旋转。我们将构建一个ANN模型， 包括两个输出和七个输入。这两个输出是来自 踝背屈肌和跖屈肌对支撑基底起始的反应 旋转这七个输入是平均眼睛-目标距离（从眼睛中心的距离 视觉目标），头部加速度（线性和角度），踝关节旋转， 踝关节旋转速度和地面反作用力（法向和剪切力） 在脚下。这些输入代表对视觉的机械刺激， 前庭和躯体感觉系统。这些输入和输出 将直接从两组老年受试者中测量变量： 外周神经病性和正常的、非外周神经病性。然后我们将 通过反向传播训练确定ANN模型中的权重 例程，以及将每个输出与每个输入相关联的相应Q值。 我们将统计比较多个感觉输入之间的Q值 在每一组中。我们假设在这个实验条件下，Q 将姿势肌肉活动与躯体感觉输入相关联的值将是：（1） 显著高于与其他感觉输入（例如， 视觉和前庭输入）;和（2） 显著低于与神经病患者的其他感觉输入相关的Q值 科目希望这项研究将有助于我们了解如何 感觉信息用于控制姿势肌肉活动，以及 运动-感觉关系的改变可导致姿势稳定性的增加 或福尔斯。