Discrete and Rhythmic Dynamics in Multipoint Movements

多点运动中的离散和节奏动力学

• 批准号: 0096543
• 负责人: Dagmar Sternad
• 金额: $ 34.29万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0096543&HistoricalAwards=false
• 关键词: Discrete; Rhythmic; Dynamics; Multipoint; Movements

This research will investigate the generation of perceptually controlled behavior in biological and artificial systems. Its focus is to understand intralimb coordination that consists of both discrete and rhythmic elements, such as in drawing or handwriting. The hypothesis underlying the work is that unconstrained multijoint movements can be understood in terms of two fundamental units of action, discrete movements and rhythmic movements. This "D-R" hypothesis is partially motivated by the fact that, from the perspective of dynamical systems theory, fixed-point and limit cycle dynamics are two primary stable regimes in a complex dynamic system. The research will involve the development of a dynamical model for multijoint movements, consisting of two separate pattern generators that produce rhythmic and discrete movement trajectories. A series of experimental studies will investigate this "D-R" hypothesis in three stages. First, the basic hypothesis that two regimes exist and that they interact will be tested in experiments examining controlled single-joint and two-joint movements that involve both rhythmic and discrete elements. Second, a subset of the same movement tasks will be examined, with additional recording of cerebral blood flow using functional magnetic resonance imaging. The "D-R" hypothesis expects that rhythmic and discrete movements will exhibit different brain activation patterns, and the research will test their interaction. Third, complex unconstrained arm movements will be studied in a three-dimensional drawing task. The behavioral experiments will conclude by testing the modeling propositions in the complex perceptual-motor skill of rhythmically bouncing a ball. Complementing the experiments, the model equations will be implemented on an anthropomorphic robot arm with seven degrees of freedom, in order to synthesize movements on the basis of the proposed organizational dynamics.This research is fundamental to understanding how humans perform their everyday activities, the vast majority of which involve coordination of multijoint movements with perceptual information. In addition, from the standpoint of complex system theory, the investigation of the human body and its central nervous system, among the most complex of systems, is extremely useful to the goal of understanding the fundamental organizational properties of complex systems. Furthermore, obtaining a deeper understanding of what could be elementary units in the control of perceptuomotor tasks has the potential to advance knowledge for diagnosis and treatment of movement disorders, as well as to advance methods of training and rehabilitation. In addition, the work on the anthropomorphic robot is ideally suited for studying control principles that can be used for the development of new technologies concerning general purpose autonomous movement systems, limb prostheses, and, in the long run, techniques for functional stimulation in patients. The planned combination of fMRI experiments and behavioral experiments will also contribute to bridging psychological and neurobiological disciplines.

本研究将探讨生物和人工系统中感知控制行为的产生。它的重点是理解由离散和有节奏的元素组成的内部协调，例如绘画或手写。这项工作背后的假设是，无约束的多关节运动可以用两个基本的动作单位来理解，即离散运动和有节奏的运动。这种“D-R”假设的部分原因是，从动力系统理论的角度来看，不动点动力学和极限环动力学是复杂动力系统的两个主要稳定状态。该研究将涉及多关节运动动力学模型的开发，该模型由两个独立的模式生成器组成，产生有节奏和离散的运动轨迹。一系列的实验研究将分三个阶段对这一“D-R”假设进行调查。首先，存在两种机制并相互作用的基本假设将在实验中得到检验，这些实验检查了涉及节奏和离散元素的受控单关节和双关节运动。其次，将检查相同运动任务的子集，并使用功能磁共振成像额外记录脑血流量。“D-R”假说认为，有节奏的和离散的运动将表现出不同的大脑激活模式，这项研究将测试它们之间的相互作用。第三，将在三维绘图任务中研究复杂的无约束手臂运动。行为实验将通过测试复杂知觉运动技能的建模命题来结束。作为实验的补充，模型方程将在具有七个自由度的拟人机器人手臂上实施，以便在提出的组织动力学的基础上合成运动。这项研究是理解人类如何进行日常活动的基础，其中绝大多数活动涉及多关节运动与感知信息的协调。此外，从复杂系统理论的角度来看，研究人体及其中枢神经系统，这是最复杂的系统之一，对于理解复杂系统的基本组织特性是非常有用的。此外，对感知运动任务控制的基本单元的深入了解，有可能促进运动障碍的诊断和治疗，以及促进训练和康复的方法。此外，拟人机器人的工作非常适合研究控制原理，这些原理可用于开发有关通用自主运动系统，肢体假肢的新技术，从长远来看，还可以用于患者的功能刺激技术。fMRI实验和行为实验的计划结合也将有助于弥合心理和神经生物学学科。