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

Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees