Leveraging over intact priors for boosting control and dexterity of prosthetic hands by amputees
This work addresses the challenge of improving control dexterity for amputees using prosthetic hands, representing an incremental advance by extending domain adaptation methods to this specific population.
The study tackled the problem of long training times for non-invasive myoelectric prostheses by evaluating domain adaptation algorithms to leverage prior training data from other subjects, finding that it reduces training time by about an order of magnitude for amputees, regardless of whether the prior data comes from amputees or intact subjects.
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.