Analysis and prediction of upper extremity movements by cyclograms

Abstract

Quantification of upper extremity movement is a common objective in both research and clinical practice. Currently, methods based on angle-angle diagrams, also called cyclograms, seem to be promising. Nevertheless, compared to the study of lower limbs, the concept of angle-angle diagrams has not been systematically used to study upper limb movements during walking. The paper describes two examples of new methods based on angle-angle diagrams for application in rehabilitation and assistive robotics. The cyclograms represent information about the relationship between the angles and their changes over time. We used cyclograms as patterns for learning artificial neural networks and predicting the movement of upper-limb. Together with artificial intelligence, cyclograms offer wide scope of application in prosthesis control systems. Using bilateral cyclogram, the information about the relationship between the right and left arm joint angles is used to evaluate the symmetry of movements. The method based on the orientation of the bilateral cyclogram can be used as an additional method for determining the symmetry of movements of the upper limbs or exo-prosthesis.