NDLI: Comparison of force and power generation patterns and their predictions under different external dynamic environments

Content Provider	IEEE Xplore Digital Library
Author	Chhatbar, P.Y. Francis, J.T.
Copyright Year	2010
Description	Author affiliation: Physiology and Pharmacology Department, and holds faculty position in Graduate Program in Neurobiology and behavior, SUNY Downstate Medical Center, Brooklyn, NY 11203, USA (Francis, J.T.) \|\| Joint Graduate Program in Biomedical Engineering between SUNY Downstate Medical Center and Polytechnic Institute of New York University at Brooklyn, 11203, USA (Chhatbar, P.Y.)
Abstract	Use of neural activity to predict kinematic variables such as position, velocity and direction etc of movements has been implemented in real-time control of robotic systems and computer cursors. In everyday life, however, we generate variable amounts of force to manipulate objects of different inertial properties or to follow the same trajectory under different external dynamic environments like air or water. The resultant work during such movements, and its time derivative power, should depend on the dynamics of the movement. In order to give the users of a brain-machine interface (BMI) comprehensive control of a prosthetic limb under different dynamic conditions, it is imperative to consider the dynamics-related parameters like end-effector forces, joint torques or power. In this paper, we show distribution patterns of two such dynamics parameters - force and power - and their predictive efficiency under different dynamic environmental conditions. We intend to find the force-related parameter, which has optimal predictive efficiency across different dynamic environments that is generalization. Our ultimate goal is to materialize a force-based brain-machine interface (fBMI).
Starting Page	1686
Ending Page	1690
File Size	497377
Page Count	5
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5626832
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Force Joints Dynamics Kinematics Torque Elbow Shoulder Neuroprosthetics Brain-Computer Interface Power Force-related Parameters Robotic Manipulandum Chronic Microelectrode Array Motor Learning Reaching Movements
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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