Biological inspired control and machine learning for clinical rehabilitation and engineering systems

用于临床康复和工程系统的生物启发控制和机器学习

• 批准号: 2326976
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2326976
• 关键词: Biological; inspired; control; machine; learning

Brief description of the context of the research including potential impact:Explaining how humans control movement and posture has been of interest to scientists and engineers for a long time, with controversy remaining about the exact nature of human motor control, and how this is related to their ability to adapt and to learn. One approach which has been developed in recent years, is based on a combination of open-loop predictive control and intermittent closed loop control, termed intermittent predictive control. This project is related to ongoing basic research in control theory, with applications in Big Data science (EPSRC project EP/R018634/1 ``Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics''). Insights into human control gained from theoretical developments will be used in applications in rehabilitation approaches. Since rehabilitation is a process of relearning, understanding how humans learn provides a solid basis for designing novel rehabilitation approaches.Aims and objectives:The aim of this project is to investigate the application of adaptation and learning based on intermittent control concepts in the context of neuro-rehabilitation. Specific objectives will be:Investigate mechanisms for human-like adaptation and learning, Evaluate the role machine learning can play in an adaptive intermittent control structure, Explore the potential of intermittent control to improve joint human-machine interaction, focusing on applications in neuro-rehabilitation.Novelty of the research methodology:Intermittent control is a relatively new control concept which is originally based on model predictive control. Its properties in the context of adaptation and learning are currently not well understood. The novel approach taken in this project will be to develop the understanding of theoretical characteristics into tools which can be used for relearning in a clinical rehabilitation context. Alignment to Research Council's strategies and research areas:Focus areas are Healthcare Technologies, specifically Optimising treatments.Any companies or collaborators involved:Clinical evaluations will be conducted in collaboration with the Queen Elizabeth National Spinal Injuries Unit at the Queen Elizabeth University Hospital, Glasgow.

简要描述研究背景​​，包括潜在影响：长期以来，科学家和工程师一直对解释人类如何控制运动和姿势感兴趣，但关于人类运动控制的确切性质以及这与他们的适应和学习能力有何关系仍然存在争议。近年来开发的一种方法基于开环预测控制和间歇闭环控制的组合，称为间歇预测控制。该项目与正在进行的控制理论基础研究及其在大数据科学中的应用相关（EPSRC 项目 EP/R018634/1“复杂、计算和数据密集型分析的闭环数据科学”）。从理论发展中获得的对人类控制的见解将用于康复方法的应用。由于康复是一个重新学习的过程，了解人类如何学习为设计新颖的康复方法提供了坚实的基础。目的和目标：该项目的目的是研究基于间歇控制概念的适应和学习在神经康复中的应用。具体目标将是：研究类人适应和学习的机制，评估机器学习在自适应间歇控制结构中可以发挥的作用，探索间歇控制改善人机联合交互的潜力，重点关注在神经康复中的应用。研究方法的新颖性：间歇控制是一个相对较新的控制概念，最初基于模型预测控制。目前尚不清楚其在适应和学习方面的特性。该项目采用的新颖方法将对理论特征的理解发展为可用于临床康复背景下重新学习的工具。与研究委员会的战略和研究领域保持一致：重点领域是医疗保健技术，特别是优化治疗。涉及的任何公司或合作者：临床评估将与格拉斯哥伊丽莎白女王大学医院的伊丽莎白女王国家脊柱损伤科合作进行。