Learning new movements after paralysis: Results from a home-based study

Abstract

Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility. The use of the BMI over one month led to increased range of motion and force at the shoulders in chronic survivors. Concurrently, subjects learned to reorganize their body motions as they practiced the control of a computer cursor to perform different tasks and games. The BMI allowed subjects to generate any movement of the cursor with different motions of their body. Through practice subjects demonstrated a tendency to increase the similarity between the body motions used to control the cursor in distinct tasks. Nevertheless, by the end of learning, some significant and persistent differences appeared to persist. This suggests the ability of the central nervous system to concurrently learn operating the BMI while exploiting the possibility to adapt the available mobility to the specific spatio-temporal requirements of each task.

Unsupervised Coadaptation of an Assistive Interface to Facilitate Sensorimotor Learning of Redundant Control

“…the structure of the task), and increase user's frustration and fatigue. Moreover, a recent work by Pierella et al [4] highlighted how task constraints also influence the learned map, in that a subject generally adopts distinct movement patterns for different tasks.…”

“…We consider BMIs, in which a linear map that performs dimensionality reduction describes the transformation from a high-dimensional space of body movement signals to a lower-dimensional space of device movements. In order to skillfully operate this type of BMI, the user must effectively learn a suitable inverse of this map, capturing the properties of the tool [4].…”

The dynamics of motor learning through the formation of internal models

“…Here, we extend the distal learning approach to the learning of a novel map established by a body machine interface (BoMI) that translates movements of the upper body (shoulders and arm) into movements of an external object that users must guide to a set of target locations. This BoMI has been shown to be an assistive tool for people that have lost the use of their hands after injury to the cervical spinal cord [15][16][17][18][19]. However, the field still lacks a mathematical description of the process that takes place while subjects are learning to proficiently use this BoMI.…”

Concurrent Validity of a Virtual Version of Box and Block Test for Patients with Neurological Disorders

“…Hence, the need to develop low-cost home devices for training and maintenance of manual dexterity that include assessment tools such as the one presented here [11]. In relation to the neurological pathologies we used for validation purposes, 10 patients suffered a cervical SCI, one patient suffered a Guillain-Barré syndrome and another suffered sequelae of an infectious meningen cephalomeningitis.…”