Individualised cognitive and motor learning for the elderly
This project aims to create a method with high efficacy aimed at providing an individualised approach to motor sequence learning in elder adults. The current problem is that the provision of motor learning programs have little/no consideration of individual cognitive and motor differences, and therefore varying levels of improvements. The innovation is to provide a management solution to the ageing demographics across Europe by creating an evidence-based approach targeting cognitive and motor learning parameters for elder adults to improve their overall motor function using an ecological dancelike sequence learning task. We propose a three-stage approach to investigate and provide a viable solution. In the first stage, we will address historical and current theoretical issues with the quantification of motor learning development in elder adults. Addressing topics such as dynamical systems and the aggregation of performance analyses in the face of a large range of baseline physical differences in the elderly will shed new light in a theoretically driven perspective. In the second stage, we will create an integrated neurocognitive model of motor learning representation for the elderly using advanced supervised machine learning with real EEG and behavioural data. Lastly, we will create and pilot an individualised intervention based on the model in the second stage, that targets cognitive control using meditation, and physical parameters with progressive chunk scaling approaches to drive greater learning outcomes in a shorter timeframe for the elderly. The results from this project have the potential to change health management policies and future applications of advanced brain computer interfaces.