Tigrini Andrea, Verdini Federica, Maiolatesi Marco, Monteriù Andrea, Ferracuti Francesco, Fioretti Sandro, Longhi Sauro, Mengarelli Alessandro
Department of Information Engineering, Università Politecnica Delle Marche, Ancona, Italy.
Front Bioeng Biotechnol. 2022 Jan 21;9:804904. doi: 10.3389/fbioe.2021.804904. eCollection 2021.
In this study, the neuromuscular control modeling of the perturbed human upright stance is assessed through piecewise affine autoregressive with exogenous input (PWARX) models. Ten healthy subjects underwent an experimental protocol where visual deprivation and cognitive load are applied to evaluate whether PWARX can be used for modeling the role of the central nervous system (CNS) in balance maintenance in different conditions. Balance maintenance is modeled as a single-link inverted pendulum; and kinematic, dynamic, and electromyography (EMG) data are used to fit the PWARX models of the CNS activity. Models are trained on 70% and tested on the 30% of unseen data belonging to the remaining dataset. The models are able to capture which factors the CNS is subjected to, showing a fitting accuracy higher than 90% for each experimental condition. The models present a switch between two different control dynamics, coherent with the physiological response to a sudden balance perturbation and mirrored by the data-driven lag selection for data time series. The outcomes of this study indicate that hybrid postural control policies, yet investigated for unperturbed stance, could be an appropriate motor control paradigm when balance maintenance undergoes external disruption.
在本研究中，通过带外部输入的分段仿射自回归（PWARX）模型评估了受干扰的人体直立姿势的神经肌肉控制建模。十名健康受试者接受了一项实验方案，其中施加了视觉剥夺和认知负荷，以评估PWARX是否可用于模拟中枢神经系统（CNS）在不同条件下维持平衡中的作用。将平衡维持建模为单连杆倒立摆；并使用运动学、动力学和肌电图（EMG）数据来拟合CNS活动的PWARX模型。模型在70%的数据上进行训练，并在属于其余数据集的30%的未见数据上进行测试。这些模型能够捕捉CNS所受的哪些因素，在每个实验条件下显示出高于90%的拟合精度。模型呈现出两种不同控制动力学之间的切换，这与对突然平衡扰动的生理反应一致，并通过数据时间序列的数据驱动滞后选择反映出来。本研究结果表明，尽管混合姿势控制策略尚未针对无干扰姿势进行研究，但当平衡维持受到外部干扰时，它可能是一种合适的运动控制范式。
