Gaussian Process Regression for accurate prediction of prosthetic limb movements from the natural kinematics of intact limbs

We propose a Gaussian Process-based regression framework for continuous prediction of the state of missing limbs by exclusively decoding missing limb movements from intact limbs - we achieve this as we have measured the correlation structure and synergies of natural limb kinematics in daily life. Using the example of hand neuroprosthetic, we demonstrate how our model can use non-linear regression to infer the velocity of the flexion/extension joints of missing fingers by observing the intact joints using a data glove. We based our framework on hand joint velocity data, that we recorded with a sensorised glove from 7 able-bodied subjects performing everyday hand movements. We then simulate missing fingers by making our regressors predict the motion that a neuroprosthetic finger should execute based on the previously observed movements of intact fingers. Perhaps surprisingly, we achieve and R² = 0.89 and an RMSE = 0.20°/s across all missing joints. Moreover, by performing one-subject-out cross validation, we can show that the prediction accuracy and precision has negligible significant loss of performance when tested on new subjects. This suggests that kinematic correlations in daily life can provide a powerful channel refining, if not driving, multi-source neuroprosthetic and Brain Computer Interface approaches.