|
|
| |
Brain-Computer Interfaces
& Neuroprostheses |
|
|
|
 |
Abstract |
 |
|
|
| Despite advances in invasive brain computer interfaces, the current staple for prosthetic control is to use electromyography (EMG). Myoelectric signals are acquired through skin surface electrodes, and involve the detection of electrical activity that corresponds to muscle contraction. Through various training protocols and signal processing techniques, limited EMG control of powered prosthetic devices is possible. Our work focuses on developing more functional control schemes as well as more intuitive interfaces for users of these powered upper-limb prostheses. |
|
|
|
| |
 |
| A subject performs an index finger extension as part of an EMG experiment. Various features extracted from the EMG signal, such as the Wilson Amplitude shown here, allows better performance in pattern recognition applications. |
|
| |
In the past, powered upper-limb prostheses have been limited mechanically by devices with very few degrees of freedom. However, the ongoing development of multi-fingered dexterous hands, and improvement in electrode technology, has precipitated the need for improved control algorithms for next generation prostheses. Using both able-bodied and amputee subjects, our research focuses on high-accuracy decoding of a wide range of discrete finger and grasp movements to improve the range of functionality.
|
| |
| Another direction is the development of algorithms for continuous decoding of hand conformation using a motion-tracking Cyberglove. This will help provide more intuitive control of multiple end-effectors at the same time. |
| By using the Cyberglove, it becomes possible to continuously track hand conformation such as the joint angle of the metacarpophalangeal joints (MCP) shown here. |
|
| |
|
Researchers |
|
|
|
David Huberdeau
Ryan Smith
Francesco Tenore, PhD - Johns Hopkins Applied Physics Lab |
| |
|
Collaborators |
|
|
|
Infinite Biomedical Technologies
Charles Dankmeyer - Dankmeyer Prosthetics
|
| |
|
Funding |
|
|
|
Defense Advanced Project and Research Agency (DARPA) - contract N66001-06-C-8005
|
| |
|
Publications |
|
|
|
Tenore F, Ramos A, Acharya S, Etienne-Cummings R and Thakor NV, Decoding of individuated finger movements using surface electromyography, IEEE Trans Biomed Eng, in press, 2008
Smith RJ, Tenore F, Huberdeau D, Thakor NV, Continuous Decoding of Finger Position from Surface EMG Signals for Control of Powered Prostheses, Conf Proc IEEE Eng Med Biol Soc, 1:197-200, 2008
Huberdeau D, Aggarwal V, Tenore F, Fritz K, Etienne-Cummings R, Thakor NV, Real-time finger tracking to improve upper-limb prosthetics control, Conf Proc Northeast Bioeng Conf, 2008
Tenore F, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV, Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals, Conf Proc IEEE Eng Med Biol Soc, 1:6145-8, 2007 |
| |
|
| |
|
|
|
| |
|
|
Copyright © 2008 JHU Neuroengineering & Biomedical Instrumentation Lab
All rights reserved. |
|
|
