  Non-invasive myoelectric prostheses require a long training time to obtain
satisfactory control dexterity. These training times could possibly be reduced
by leveraging over training efforts by previous subjects. So-called domain
adaptation algorithms formalize this strategy and have indeed been shown to
significantly reduce the amount of required training data for intact subjects
for myoelectric movements classification. It is not clear, however, whether
these results extend also to amputees and, if so, whether prior information
from amputees and intact subjects is equally useful. To overcome this problem,
we evaluated several domain adaptation algorithms on data coming from both
amputees and intact subjects. Our findings indicate that: (1) the use of
previous experience from other subjects allows us to reduce the training time
by about an order of magnitude; (2) this improvement holds regardless of
whether an amputee exploits previous information from other amputees or from
intact subjects.
