Human movement modeling and understanding: biomedical, biometrical and neurocognitive applications.

1-内容：人体运动建模对于依赖于对精细运动控制的理解的模式识别系统的设计以及对于以某种方式涉及人体运动处理的智能系统的开发具有极大的兴趣。在提供轨迹的分析表示的模型中，快速人体运动的运动学理论及其对数正态模型族已被用于解释人类运动控制的经典研究中报道的大多数基本现象，并研究涉及精细运动的基本因素。多年来，我们的团队开发了几个软件包，用于在不同的实验数据采集条件和设置下从各种笔尖速度曲线中提取对数正态参数。这些不同的工具允许研究人员通过参与给定任务的神经肌肉网络的参数对数正态描述来研究笔迹。2-目标：本提案旨在详细说明任何手写处理应用程序的基础和理论背景，并提供一些基本知识，可以集成到三个应用领域的许多自动系统的开发中：生物医学：我们将专注于影响神经运动控制的三种主要疾病：脑血管意外，帕金森病和阿尔茨海默病。我们还将研究孩子们如何学习手写慢慢移动和掌握他们的运动控制行为。生物统计学：我们将专注于签名验证和作者识别，以指出哪些神经肌肉特征的组合最能代表一个人的写作和签名习惯。神经认知：我们将专注于将理论扩展到眼球运动，将视觉反馈纳入模型，将理论推广到3D运动建模，并将整个涌现方法置于通用和全球物理模型的背景下。3-方法和战略。我们的方法是基于利用我们的参数提取算法来研究神经肌肉系统的行为在不同的实验条件下，不同的实际应用。由于有许多潜在的途径可以探索（这是一个孤立的团队无法完成的），我们最近与各种国家和国际合作伙伴签署了几项研究合作协议。这些协议允许我们的合作者在非营利研究项目中使用我们的软件，我们的团队在这些项目中扮演着积极合作的关键角色，为许多学生提供成功使用我们的技术和研究方向的基本知识，从而形成高素质的人才。4-影响。作为培训众多学生和研究人员的一部分，整个计划将导致神经运动建模的重要理论突破，并为模式识别三个利润丰厚的领域中众多自动或交互式设备的设计奠定知识背景。这可以通过支持我们研究的愿景的典型实现来说明。在未来的几年里，人们使用智能手持设备的手势和手写习惯将在我们的神经肌肉模型的背景下被他们自己的设备监控和分析。这些系统将能够识别其用户，保护其设备和敏感数据，跟踪其精细运动控制的演变，以检查可能需要医疗干预的任何偏离对数正态性。这些新工具将促进人机交互在众多应用中，手写和绘图仍然是最自然和符合人体工程学的沟通方式。

1-Context : Human movement modeling is of great interest for the design of pattern recognition systems relying on the understanding of the fine motor control as well as for the development of intelligent systems involving in a way or another the processing of human movements. Among the models providing analytical representations of the trajectories, the Kinematic Theory of rapid human movements and its lognormal families of models have been used to explain most of the basic phenomena reported in classical studies on human motor control and to study basic factors involved in the fine motricity. Several software packages have been developed by our team over the years to extract the lognormal parameters from various pen tip velocity curves under different experimental data acquisition conditions and set-up. These different tools allow a researcher to study handwriting through the parametric lognormal description of the neuromuscular networks involved in a given task. 2-Goal: The present proposal aims at elaborating a fundamental and theoretical background for any handwriting processing applications as well as providing some basic knowledge that can be integrated in the development of many automatic systems in three fields of applications: Biomedical: We will focus on three main diseases that affect neuromotor control: Cerebrovascular accidents, Parkinson disease and Alzheimer disease. We will also study how children learning handwriting slowly move and master their motor control behavior. Biometrical: We will focus on signature verification and writer identification to point out which combinations of neuromuscular features are the most representative of a person writing and signing habits. Neurocognitive: We will focus on extending the theory to eye movements, incorporating visual feedback to the models, generalizing the theory to the modeling of 3D movements and putting the whole emergent approach in the context of a universal and global physical model. 3-Methodology and strategy. Our methodology is based on the exploitation of our parameter extraction algorithms to study the behavior of the neuromuscular system under various experimental conditions for different practical applications. Since there are numerous potential pathways that can be explored (which cannot be done by a single isolated team), we have signed recently several research cooperation agreements with various national and international partners. These agreements allows our collaborators to use our software in the context of non-profit research projects in which our team plays the key role of an active associate providing basic knowledge to successfully use our technology and direction in research to many students, resulting in the formation of highly qualified personnel. 4-Impact. A part from training numerous students and researchers, the whole program will lead to important theoretical breakthroughs in neuromotor modeling and set the knowledge background for the design of numerous automatic or interactive devices in three lucrative fields of pattern recognition. This can be illustrated by a typical actualization of the vision that supports our research. In years to come, the gesture and the handwriting habits of people using intelligent handheld devices will be monitored and analyzed by their own apparatuses, in the context of the our neuromuscular models. These systems will be able to identify their users, protect their equipment and sensitive data, to follow the evolution of their fine motor control to check for any departure form lognormality that might necessitate medical intervention. These new tools will facilitate man machine interactions in numerous applications where handwriting and drawing remains the most natural and ergonomic way to communicate.