Deficit Fields for Stroke Recovery

Summary

This study investigates the potential of customized robotic and visual feedback interaction to improve recovery of movements in stroke survivors. While therapists widely recognize that customization is critical to recovery, little is understood about how take advantage of statistical analysis tools to aid in the process of designing individualized training. Our approach first creates a model of a person's own unique movement deficits, and then creates a practice environment to correct these problems. Experiments will determine how the deficit-field approach can improve (1) reaching accuracy, (2) range of motion, and (3) activities of daily living. The findings will not only shed light on how to improve therapy for stroke survivors, it will test hypotheses about fundamental processes of practice and learning. This study will help us move closer to our long-term goal of clinically effective treatments using interactive devices.

Conditions

• Stroke

Interventions

BEHAVIORAL

Deficit-fields to reduce error

Stroke survivors exhibit error in both reaching extent and abnormal curvatures of motion. Prior error augmentation techniques multiply error by a constant at each instant during movement. However, magnification of spurious errors may provoke over-compensation. We hypothesize that a deficit-field design, using the statistics of a patient's errors to customize training, will provide optimal augmentation that varies during motion as needed. We will compare the training effects of error deficit-fields with previous methods of error augmentation to improve reaching ability.

BEHAVIORAL

Deficit-fields to expand range of motion

Motor deficits manifest in the workspace limitations of joints, i.e. reduced range of motion, uneven extension-flexion, inter-joint coupling, and unwanted synergies. Our work builds upon these ideas by augmenting self-directed movement for training coordination. We found that amplifying augmentation can expand motor exploration and improve skill retention in patients. Using motor exploration patterns from each patient, we will form customized deficit-fields to recover normal joint workspace. We will compare augmentation training that either amplifies or diminishes the observed deficits (Expt-1). We also compare deficit-fields with our prior augmentation methods to determine the added value of increased customization (Expt-2).

BEHAVIORAL

Deficit-fields to improve function

Clinicians have recognized the benefits of training on everyday tasks (Hubbard, Parsons et al. 2009), as well as practice with whole-body actions (Boehme 1988; Bohannon 1995). However, typical robotic systems have only a single contact point and cannot drive the multiple joints involved in functional tasks. Visual distortions (e.g. a shift, rotation or stretch) can promote adaptation even without forces. Here we present visual distortion of whole body movement during manual tasks during standing, including reaching, grasping, and object manipulation. We compare the training effects of feedback based on deficit-fields versus practice with normal vision.

Sponsors & Collaborators

• National Institutes of Health (NIH)

  collaborator NIH

• National Institute of Neurological Disorders and Stroke (NINDS)

  collaborator NIH

• Shirley Ryan AbilityLab

  lead OTHER

Principal Investigators

• James L Patton, PhD · Shirley Ryan AbilityLab

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

100 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2013-05-01

Primary Completion

2019-06-30

Completion

2019-06-30

Countries

• United States

Study Locations