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.
Keywords: force control; human-machine interaction; human-machine interfaces; incremental learning; rehabilitation robotics; ultrasound imaging.