Grant: Research Tools and Instruments - Category 1 (<$150,000)
University of New Brunswick
$85,702.00
Myoelectric signal source separation for prosthetic control
EF 2006-07
Résumé
This research focuses on the functional replacement of the wrist and hand, in the context of an individual who has an amputation (or congenital deficiency) below the elbow. This represents almost two-thirds of the amputee population. For those with upper limb deficiencies, currently available prosthetic limbs offer limited function. This is due, in large part, to the lack of control information that can be delivered to the device by the user. Significant advances have been made in recent years in interpreting a user's intent by monitoring the myoelectric signal at multiple sites, and using advanced signal processing to determine the intended motion. The applicants have demonstrated that it is possible to control wrist and hand function with high reliability using a pattern recognition approach. Although this represents a significant advance, pattern recognition allows only one function to be actuated at a given time. This requires a user to manipulate one joint at a time; if a task requires multiple joints, these must be done in sequence. Clearly this is not how one uses an intact limb. This research promises simultaneous and independent control of multiple joints to deliver natural, coordinated motion to prosthetic devices. This will be accomplished using a combination of source separation methods and inverse modeling to resolve the activity the forearm muscles. By determining the level of activity of individual muscles which act as the "prime movers" in hand and wrist manipulation, each joint may be controlled independently. The ability to control hand and wrist function in a natural, coordinated manner will represent a tremendous enhancement in prosthetic control.
Programme
Research Tools and Instruments - Category 1 (<$150,000)
Bénéficiaire
Emplacement
New Brunswick, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
345026-2007
Name nom
Englehart, Kevin
Department département
Electrical and Computer Engineering
Organization id
75
Institution établissement
University of New Brunswick
Province en
New Brunswick
Province fr
Nouveau-Brunswick
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2006
Competition year année de concours
2007
Award amount
85702
Program id
EQPEQ
Program name en
Research Tools and Instruments - Category 1 (<$150,000)
Program name fr
Outils et instruments de recherche - catégorie 1 (<150 000$)
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
335
Committee name en
Electromag. & Electr. Systems (H)
Committee name fr
Électromagn. et syst. électr. (H)
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1900
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical engineering
Research subject fr
Génie biomédical
Partie
1
Nb partie
1
Application title
Myoelectric signal source separation for prosthetic control
Keyword
NA
Application summary
This research focuses on the functional replacement of the wrist and hand, in the context of an individual who has an amputation (or congenital deficiency) below the elbow. This represents almost two-thirds of the amputee population. For those with upper limb deficiencies, currently available prosthetic limbs offer limited function. This is due, in large part, to the lack of control information that can be delivered to the device by the user. Significant advances have been made in recent years in interpreting a user's intent by monitoring the myoelectric signal at multiple sites, and using advanced signal processing to determine the intended motion. The applicants have demonstrated that it is possible to control wrist and hand function with high reliability using a pattern recognition approach. Although this represents a significant advance, pattern recognition allows only one function to be actuated at a given time. This requires a user to manipulate one joint at a time; if a task requires multiple joints, these must be done in sequence. Clearly this is not how one uses an intact limb. This research promises simultaneous and independent control of multiple joints to deliver natural, coordinated motion to prosthetic devices. This will be accomplished using a combination of source separation methods and inverse modeling to resolve the activity the forearm muscles. By determining the level of activity of individual muscles which act as the "prime movers" in hand and wrist manipulation, each joint may be controlled independently. The ability to control hand and wrist function in a natural, coordinated manner will represent a tremendous enhancement in prosthetic control.