Exoskeleton application to assist learning of a coincident timing motor task of the arm using passive mechanical perturbations

Author(s)

Moura, R. T. ; Souza, R. S. ; Garcia, E. ; Quadrado, V. H. ; Villalpando, M. B. ; Forner-Cordero, A. ; IEEE

• Document type: Journal article
• Source: 2016 6TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB); v. N/A, p. 5-pg., 2016-01-01.

Abstract

One of the key aspects in exoskeleton design for neurological rehabilitation is related to the definition of a certain dynamics that improves or accelerates the learning of a desired movement or a rehabilitation process. In order to assess the process of learning or adaptation of a coincident timing task, we have used an exoskeleton-based experiment applying different dynamics. The experimental task consisted of intercepting a moving virtual target displayed on the screen. The interception area was defined simultaneously in a virtual and a real environment. The real target was marked on a table, in such a way that the subject wearing the elbow exoskeleton could reach the target by performing an elbow flexion. The task goal was to position the hand over a marker placed on the table at the same time as the virtual object ascended to the virtual interception area on the computer screen. Hand motion data were acquired by an inertial sensor (VN-100, VectorNAv) attached to the subject's hand while electromyography (EMG, FreeEMG, BTS) was used to record the activity of the following muscles: biceps brachii, triceps brachii and brachioradialis to assess muscle response. Our research focused on the percentage of correct responses and the tendency of correct responses. We also monitored smoothness of the movement and correlated our observations with EMG activities. The preliminary results indicate that the addition of a mechanical predictable load improves the learning rate. (AU)

• FAPESP's process: 10/17181-0 - Motor control analysis of the upper limb Phase 1 - development of a biomimetic robotic exoskeleton
• Grantee: Arturo Forner Cordero
• Support Opportunities: Regular Research Grants