  Background: Multi-articulate prostheses are capable of performing dexterous
hand movements. However, clinically available control strategies fail to
provide users with intuitive, independent and proportional control over
multiple degrees of freedom (DOFs) in real-time. New Method: We detail the use
of a modified Kalman filter (MKF) to provide intuitive, independent and
proportional control over six-DOF prostheses such as the DEKA "LUKE" Arm. Input
features include neural firing rates recorded from Utah Slanted Electrode
Arrays and mean absolute value of intramuscular electromyographic (EMG)
recordings. Ad-hoc modifications include thresholds and non-unity gains on the
output of a Kalman filter. Results: We demonstrate that both neural and EMG
data can be combined effectively. We also highlight that modifications can be
optimized to significantly improve performance relative to an unmodified Kalman
filter. Thresholds significantly reduced unintended movement and promoted more
independent control of the different DOFs. Gain were significantly greater than
one and served to amplify participant effort. Optimal modifications can be
determined quickly offline and translate to functional improvements online.
Using a portable take-home system, participants performed various activities of
daily living. Comparison with Existing Methods: In contrast to pattern
recognition, the MKF allows users to continuously modulate their force output,
which is critical for fine dexterity. The MKF is also computationally efficient
and can be trained in less than five minutes. Conclusions: The MKF can be used
to explore the functional and psychological benefits associated with long-term,
at-home control of dexterous prosthetic hands.

Neurons and Cognition;Robotics