Eye-hand coordination assessment method using a haptic virtual environment with a complex valued neural networks training algorithm

Post-stroke patients usually undergo therapy that focuses on general gross-motor movements such as walking, balancing, involving lower limbs; or general arm movements, leaving them with fine-dexterity and eye-hand coordination problems at their chronic stage. In this paper, we discuss the possibility to come up with assessment metrics for eye-hand coordination therapy, using a mapping between a robotic haptic device to a virtual environment and a training algorithm based on Complex Valued Neural Networks that will determine how close a determined movement pattern is in relationship with that traced by a healthy individual. Most of the current robotic systems' therapy relies on the patient's performance on standardized clinical tests such as the functional independence measure (FIM), motor power score, and the upper limb subsection of the Fugl-Meyer (FM) scales. These systems don't have other standardized metrics for assessment purposes. There is a need to establish a more intelligent and tailored therapy that could be implemented for patients to use at home in between therapy sessions, or in the long term. This therapy should be based on performance data gathered by the robotic/computer system that will provide an assessment procedure with improved objectivity and precision. This paper presents a preliminary design and simulation results of virtual environment tasks that interface with a haptic robotic device, as well as the training of a complex valued based neural network using patterns traced by healthy individuals. The idea is to use this trained algorithm, to identify in the future, how close the patients' fine motor movements are to the healthy subject's fine coordination movements; and determine a form of metrics that can be used for assessment and future therapy decisions.