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

TL;DR

This study demonstrates that domain adaptation algorithms significantly reduce training time for myoelectric prosthetic control, with prior experience from either amputees or intact subjects being equally beneficial.

Contribution

It evaluates the effectiveness of domain adaptation algorithms in reducing training time for amputees and intact subjects, showing prior data from either group is equally useful.

Findings

Training time reduced by about tenfold using prior data.

Prior experience from amputees or intact subjects is equally effective.

Domain adaptation improves prosthetic control training efficiency.

Abstract

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.