Linear Domain Adaptation for Robustness to Electrode Shifts

Machine learning approaches have shown impressive achievements in bionic prostheses control.However, translating the machine learning models from labs to patient's everyday lives remains a challenge due to various disturbances, such as electrodes shifts.To mitigate the influence of electrode shifts, we investigate two linear domain adaptation methods and a robust training approach.In experiments, we compare all methods on both simulated electrode shifts on the Ninapro DB2 data set as well as real electrode shifts on Ninapro DB8.We find that linear domain adaptation could estimate the shift and reduce the impact of electrodes shift best, but robust training approaches similar performance without the need for new data.