Paper Info
Title
A realistic implementation of ultrasound imaging as a human-machine interface for upper-limb amputees.
Abstract
In the past years, especially with the advent of multi-fingered hand prostheses, the rehabilitation robotics community has tried to improve the use of human-machine interfaces to reliably control mechanical artifacts with many degrees of freedom. Ideally, the control schema should be intuitive and reliable, and the calibration (training) short and flexible. This work focuses on medical ultrasound imaging as such an interface. Medical ultrasound imaging is rich in information, fast, widespread, relatively cheap and provides high temporal/spatial resolution; moreover, it is harmless. We already showed that a linear relationship exists between ultrasound image features of the human forearm and the hand kinematic configuration; here we demonstrate that such a relationship also exists between similar features and fingertip forces. An experiment with 10 participants shows that a very fast data collection, namely of zero and maximum forces only and using no force sensors, suffices to train a system that predicts intermediate force values spanning a range of about 20 N per finger with average errors in the range 10-15%. This training approach, in which the ground truth is limited to an "on-off" visual stimulus, constitutes a realistic scenario and we claim that it could be equally used by intact subjects and amputees. The linearity of the relationship between images and forces is furthermore exploited to build an incremental learning system that works online and can be retrained on demand by the human subject. We expect this system to be able in principle to reconstruct an amputee's imaginary limb, and act as a sensible improvement of, e.g., mirror therapy, in the treatment of phantom-limb pain.
Year
DOI
Venue
2013
10.3389/fnbot.2013.00017
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
ultrasound imaging,human-machine interaction,human-machine interfaces,rehabilitation robotics,force control,incremental learning
Data collection,Kinematics,Computer science,Simulation,Incremental learning,Linearity,Ultrasound imaging,Ground truth,Artificial intelligence,Rehabilitation robotics,Image resolution,Machine learning
Journal
Volume
ISSN
Citations 
7
1662-5218
10
PageRank 
References 
Authors
0.95
5
2
Name
Order
Citations
PageRank
David S. González1335.84
Claudio Castellini2565.26