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

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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.