Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
This paper presents a framework for shared, human-machine control of a prosthetic arm. The method employs electromyogram and peripheral neural signals to decode motor intent, and incorporates a higher-level goal in the controller to augment human effort. The controller derivation employs Markov Decision Processes. The system is trained using a gradient ascent approach in which the policy is parameterized using a Kalman Filter and the goal is incorporated by adapting the Kalman filter output online. Results of experimental performance analysis of the shared controller when the goal information is imperfect are presented in the paper. These results, obtained from an amputee subject and a subject with intact arms, demonstrate that a system controlled by the human user and the machine together exhibit better performance than systems employing machine-only or human-only control.more » « less
-
This paper presents a framework for modeling neural decoding using electromyogram (EMG) and electrocorticogram (ECoG) signals to interpret human intent and control prosthetic arms. Specifically, the method of this paper employs Markov Decision Processes (MDP) for neural decoding, parameterizing the policy using an artificial neural network. The system is trained using a modification of the Dataset Aggregation (DAgger) algorithm. The results presented here suggest that the approach of the paper performs better than the state-of-the-art.more » « less
