There are approximately 500,000 upper extremity amputees currently living in the United States;with 18,000 new upper extremity amputees added each year. The loss of an upper limb causes a person's quality of life to plummet and brings about massive physical and psychosocial challenges. The majority of amputees are hampered by restricted functionality and use a mechanical hook or a passive, cosmetic hand. Electric Grabber hands are available, but their use is limited due to a cumbersome control mechanism. Therefore, we propose to develop a noninvasive Surface EMG Decoder And Controller (SEDAC) for use in currently available Electric Grabber hands. It employs a feature extractor and an artificial neural network classifier to estimate intended hand movements. This will enable intuitive control of the prosthetic hand. Our Phase I effort is focused on the development and validation of SEDAC for real-time decoding and control of a two-function prosthesis. This offers three key advantages over current technology: 1) intuitive activation of muscle groups for each kind of movement;2) smooth transition from one movement to another;and 3) a learning capacity which transfers the burden of training from the patient to the prosthetic. Our Phase II effort builds upon this to incorporate dimensionality reducing algorithms to improve accuracy, reduce latency, and enable intuitive control of 4 additional hand functions. This will allow for actuation of next- generation dexterous prosthetic hands which are currently under development. Through these advances, we hope to bring about a much needed improvement in the quality of life for upper extremity amputees.

Public Health Relevance

This project will provide trans-radial amputees with intuitive control of current generation prosthetics using surface electromyography (EMG). The technology will also provide a foundation for surface EMG control of fully dexterous prosthetics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
5R44NS065495-03
Application #
7923789
Study Section
Special Emphasis Panel (ZRG1-SSMI-Q (10))
Program Officer
Ludwig, Kip A
Project Start
2008-09-15
Project End
2013-02-28
Budget Start
2010-09-01
Budget End
2013-02-28
Support Year
3
Fiscal Year
2010
Total Cost
$614,890
Indirect Cost
Name
Infinite Biomedical Technologies, LLC
Department
Type
DUNS #
037376022
City
Baltimore
State
MD
Country
United States
Zip Code
21218
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Beaulieu, Robert J; Masters, Matthew R; Betthauser, Joseph et al. (2017) Multi-position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control. J Prosthet Orthot 29:54-62
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