Development of a parameterised mathematical model for hand/digit motion

1=辅助技术、康复和肌肉骨骼生物力学2=医疗保健技术这个项目的目标是开发上肢的参数化数学模型，特别是手部运动，以支持设计、制造和验证具有集成机械触觉反馈的负担得起的身体动力假肢指尖。该项目将是华威大学和伦敦大学学院(UCL)之间更大规模的PROLIMB合作项目的一部分，该项目由EPSRC资助。已经开发了各种手及其运动的数学模型，使用反向运动学来根据已知或期望的指尖位置来计算关节角度。这些技术的局限性包括模拟中的重叠和碰撞，以及模型和实际手部关节约束之间的不匹配，而且这些方法通常不是为手部运动中存在的多个最终目标而设计的。其他为生成包含上肢肌力的动态响应而开发的模型不适合我们的目的，主要是因为它们的建模假设、模型参数化或在任何误差和计算时间之间进行不切实际的权衡。最近开发的肌肉骨骼模型也被证明是不稳定的，因此不适合用作上肢正向建模方法的控制器，但仅被证明对少数抓取有效，验证有限。因此，目前的技术水平是可观的，但仍然需要一种新的机械数学模型，能够有力地描述人类的手运动并掌握分类学，从而作为设计有效的上肢和手指假体的平台。这种模型经过可靠的参数设计和验证，具有预测能力，将改变我们设计有效的手和手指假体的能力。更重要的是，它将允许他们通过个性化的参数设置进行个性化。这些都是这个博士项目的目标，开发这样一个健壮的、参数化的手部运动机械模型，可以解释现代日常抓取分类。该项目将开发机械模型，以动态描述和重现手的运动和关键抓取分类。这个模型将被开发为允许个性化的、模拟的预测，也可以整合触觉反馈。该模型将使用华威大学维康运动捕捉实验室收集的数据进行验证。这项任务的另一个结果将是模型的相应3D实现，以应用于领先的运动捕获系统中，用于手部运动捕获和分析。此外，这些模型将与伦敦大学学院项目合作者提供的指尖传感器信号相结合，以便在应用于身体动力假体时考虑与环境的物理相互作用，以便更深入地了解感觉在抓握运动中的作用，并帮助康复和个性化假体操作。

1=Assistive technology, rehabilitation and musculoskeletal biomechanics2=Healthcare technologiesThe aim of this project is to develop parameterised mathematical models of upper limb, and in particular hand motion that will support the design, fabrication and validation of an affordable body-powered prosthetic fingertip digit with integrated mechanical haptic feedback. This project will form part of the larger scale PROLIMB collaborative project between the University of Warwick and University College London UCL), funded by the EPSRC. A variety of mathematical models of the hand and its motion have been developed using inverse kinematics to calculate joint angles from the known, or desired, location of the fingertip. Limitations of these techniques include overlaps and collisions in simulation, as well as mismatches between the models and actual human hand joint constraints plus such approaches are typically not designed for the multiple end goals that exist in hand motion. Other models developed to generate dynamic responses incorporating upper limb muscle forces are unsuited to our purpose due mainly to their modelling assumptions, model parameterisations or an impractical trade-off between any errors and computation time. More recently developed musculoskeletal models have also proven to be unstable and thus unsuitable for use as a controller for upper limb Forward modelling approaches have also been adopted but only proved effective for a small number of grasps and have limited validation. The state of the art, then, is substantial but there remains an indisputable need for a novel mechanistic mathematical model that can robustly characterise human hand movements and grasp taxonomies and thus serve as a platform for the design of effective upper limb and digit prostheses. Robustly parameterised and validated to function in a predictive capacity, such a model would transform our ability to design effective hand and digit prostheses. What's more, it would permit their personalisation via individualised parameterisation. These are the aims of this PhD project, to develop such a robust, parameterised mechanistic model of hand motion that can account for modern everyday grasp taxonomies. The project will develop mechanistic models to dynamically characterise and reproduce hand movements and key grasp taxonomies. This model will be developed to permit personalised, simulated predictions that can also integrate haptic feedback. The model will be validated using data collected in Vicon motion capture laboratories at the University of Warwick. An additional outcome of this task will be a corresponding 3D implementation of the model for application within a leading motion capture system for hand motion capture and analysis. In addition, these models will be integrated with signals from fingertip sensors provided by project collaborators at UCL in order to consider physical interaction with the environment when applied to body-powered prostheses, to permit a deeper understanding of the role of sensation in grasp movements and to aid rehabilitation and personalised prostheses operation.