In this section
@article{Xiloyannis:2017:10.1109/TNSRE.2017.2699598,
author = {Xiloyannis, M and Gavriel, C and Thomik, AA and Faisal, AA},
doi = {10.1109/TNSRE.2017.2699598},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {1785--1801},
title = {Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics},
url = {http://dx.doi.org/10.1109/TNSRE.2017.2699598},
volume = {25},
year = {2017}
}
TY - JOUR
AB - Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process (gP) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our gP approach achieves high levels of performance (RMSE of 8°/s and ρ = 0.79). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of
AU - Xiloyannis,M
AU - Gavriel,C
AU - Thomik,AA
AU - Faisal,AA
DO - 10.1109/TNSRE.2017.2699598
EP - 1801
PY - 2017///
SN - 1534-4320
SP - 1785
TI - Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics
T2 - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR - http://dx.doi.org/10.1109/TNSRE.2017.2699598
UR - https://ieeexplore.ieee.org/document/8023871
UR - http://hdl.handle.net/10044/1/42557
VL - 25
ER -