Résumé
Although remarkable improvements have been
made, the natural control of hand prostheses in everyday life
is still challenging. Changes in limb position can considerably
affect the robustness of pattern recognition-based myoelectric
control systems, even if various strategies were proposed to
mitigate this effect. In this paper, we investigate the possibility
of selecting a set of training movements that is robust to limb
position change, performing a trade-off between training time
and accuracy. Four able-bodied subjects were recorded while
following a training protocol for myoelectric hand prostheses
control. The protocol is composed of 210 combinations of
arm positions, forearm orientations, wrist orientations and
hand grasps. To the best of our knowledge, it is among the
most complete including changes in limb positions. A training
reduction paradigm was used to select subsets of training
movements from a group of subjects that were tested on the
left-out subject’s data. The results show that a reduced training
set (30 to 50 movements) allows a substantial reduction of
the training time while maintaining reasonable performance,
and that the trade-off between performance and training time
appears to depend on the chosen classifier. Although further
improvements can be made, the results show that properly
selected training sets can be a viable strategy to reduce the
training time while maximizing the performance of the classifier
against variations in limb position.