Real-time movement prediction for improved control of neuroprosthetic devices

Replacing lost hands with prosthetic devices that offer the same functionality as natural limbs is an open challenge, as current technology is often limited to basic grasps by the low information readout. In this work, we develop a probabilistic inference-based method that allows for improved control of neuroprosthetic devices. We observe the behaviour of the undamaged limb to predict the most likely actions of lost limbs. Offline, our algorithm learns movement primitives (e.g. various types of grasps) from a database of recordings from healthy subjects performing everyday activities. Online, it performs Bayesian inference to determine the currently active movement primitive from the observed limbs and estimates the most likely movement of the missing limbs from the training data. We can demonstrate on test data that this two-stage approach yields statistically significantly higher prediction accuracy than linear regression approaches that reconstruct limb movements from their overall correlation structure.