Development of the control system of a low-cost robot for upper limb home rehabilitation of stroke patents

Stroke is the leading cause of disability in the UK and is expected to increase in prevalence due to an aging population. Stroke patient outcome is improved by early and intense physiotherapy after Stroke, but NHS services are increasingly under strain, particularly in the wake of the COVID 19 pandemic. Over the last thirty years there has been a great deal of development in the use of robotic devices to provide rehabilitation to Stroke patients. The system architecture of a rehabilitation robot is well defined, with a High-Level Controller generating rehabilitation tasks, a trajectory generation procedure to encode the tasks and a Low-Level Controller to implement the tasks. All commercially available rehabilitation robots are high cost, however, and a recent study has found that the cost benefit ratio is too poor to be viable [1]. Further to this, in the wake of the pandemic there has been increased interest in home-based devices. There has been research into low cost devices, but this area of research has not been sufficiently explored. MyPAM is low-cost upper-limb rehabilitation robot designed for home use. There have been two previous iterations, and the first version of MyPAM was proven to improve the outcome of Stroke patients in trials. The current system has been rearchitected, with much focus on the Low Level controller which has been implemented on lower cost hardware than previous iterations. Responsibility for trajectory generation has been moved from the High-Level controller to the Low level controller, and a novel method for affecting the trajectory with Attractors and Repulsors has been designed and validated, which has important implications on patient motivation. A multidomain dynamic model is presented, which is necessary for creating a baseline against which to compare patient performance. A novel integrated end-effector/2-axis force sensor and a novel end-effector/Tristate grip sensor are presented, both based on the MagOne sensing methodology. An Admittance controller with instability protection is presented, and the system integration is discussed.