Body Worn EMG Controlling of Assistive Robotic Arm for Stroke Rehabilitation

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$161,303.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
1R41NS081857-01A1
Award Id:
n/a
Agency Tracking Number:
R41NS081857
Solicitation Year:
2013
Solicitation Topic Code:
NINDS
Solicitation Number:
PA12-089
Small Business Information
6901 E FISH LAKE RD, STE 190, MAPLE GROVE, MN, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
927303412
Principal Investigator:
GARY HAVEY
(763) 463-4814
ghavey@ame-corp.com
Business Contact:
TOM HENDRICKSON
(763) 515-5353
thendrickson@ame-corp.com
Research Institute:
REHABILITATION INSTITUTE OF CHICAGO

REHABILITATION INSTITUTE OF CHICAGO
345 E SUPERIOR ST Research Administration
CHICAGO, IL, 60611-
() -
Domestic nonprofit research organization
Abstract
DESCRIPTION (provided by applicant): Stroke is the leading cause of adult disability and the third leading cause of death in the United States. Approximately 15 million people in the world, and more than 700,000 people in the United States, experience a stroke each year. Following a hemispheric stroke, motor control of extremities on one side of the body is usually affected. Many patients suffer a variety of disabling physical symptoms on the contralesional side of the body. In particular, upper limb (arm,hand, finger/thumb) dexterity is often affected, limiting fundamental activities of daily living (ADL) such as eating, dressing, writing or typing. A number of mechatronic devices have been designed as assistive tools for robot-aided stroke rehabilitationto improve upper limb function. Most of the devices can assist users to perform exercises which involve repetitive movement of their paretic limb in a passive way (as they relax), or in an active way (as they intend to contribute to the movement). Surfaceelectromyogram (EMG) signals contain rich motor control information, from which the user's intention can be detected. Due to the upper-limb dexterity, however, most functional tasks are generally accomplished through complex temporal and spatial coordination of multiple muscles. It is unfeasible to realize the control of such multiple DOFs via one-to-one mapping (between a muscle and a DOF). Pattern recognition techniques have recently attracted increasing attention in the development of myoelectric controlsystems. Recently, we have presented a novel framework for stroke survivors using high density surface EMG recording and pattern recognition analysis. Our research demonstrates that high accuracies can be obtained in classification of up to 20 arm, hand,finger/thumb movements involving the affected limb, suggesting that with myoelectric pattern recognition techniques substantial motor control information can be extracted from the paretic muscles of stroke subjects. Such information will potentially enablevolitional control of assistive devices, thereby facilitating the functional restoration for the affected limb. In phase I, we will demonstrate the feasibility of a high densit EMG system for robot control to allow stroke subjects volitional control of assistive tools. In phase II, the myoelectric control system will be fully integrated with the assistive robot for implementing improved stroke rehabilitation and tested on a patient population. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Itis estimated that there are 4.8 million stroke survivors in the United States and this number is likely to increase as the baby boomers age beyond 65 years over the next 2 decades. Studies have shown that stroke has a detrimental effect on health-related quality of life due to issues such as reduced upper limb (arm, hand, finger/thumb) dexterity on the contralesional side, limiting fundamental activities of daily living. It has been shown that the paralyzed arm can be improved through repetitive training ofisolated movements

* information listed above is at the time of submission.

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