Polynomial Kalman filter for myoelectric prosthetics using efficient kernel ridge regression

Abstract

This paper presents a polynomial ridge regression algorithm with substantial improvements in computational efficiency compared with the polynomial kernel ridge regression and the standard polynomial regression. This regression algorithm was combined with a Kalman Filter (KF) to yield the Directly Weighted Polynomial Ridge Regression KF (DWPRR-KF). Experiments conducted offline from data collected from a human amputee demonstrated that compared with a linear KF, the DWPRR-KF significantly reduced median range-normalized Root Mean Square Error (RMSE) caused by movement on Degrees Of Freedom (DOFs) that the user intended to hold stationary during movement of other DOFs by 63% (from 0.061 to 0.023), while insignificantly increasing median error on DOFs the user intended to move (3%; from 0.139 to 0.144). Furthermore, the median overall error, from DOFs with or without intended movement, decreased by 27% (from 0.085 to 0.063) but this change was not found significant.