  A number of studies have proposed to use domain adaptation to reduce the
training efforts needed to control an upper-limb prosthesis exploiting
pre-trained models from prior subjects. These studies generally reported
impressive reductions in the required number of training samples to achieve a
certain level of accuracy for intact subjects. We further investigate two
popular methods in this field to verify whether this result equally applies to
amputees. Our findings show instead that this improvement can largely be
attributed to a suboptimal hyperparameter configuration. When hyperparameters
are appropriately tuned, the standard approach that does not exploit prior
information performs on par with the more complicated transfer learning
algorithms. Additionally, earlier studies erroneously assumed that the number
of training samples relates proportionally to the efforts required from the
subject. However, a repetition of a movement is the atomic unit for subjects
and the total number of repetitions should therefore be used as reliable
measure for training efforts. Also when correcting for this mistake, we do not
find any performance increase due to the use of prior models.
