An automated tuning algorithm was developed to reduce the time and ski
ll required to tune a closed-loop hand grasp neuroprosthesis. The time
reduction results from simultaneous tuning of four gain parameters co
ntrolling the dynamic response of the system, and from automation of t
he calculation and decision processes. The new tuning method is theref
ore an automated parallel tuning method, replacing a manual sequential
method in which only one parameter at a time was tuned. RMS error bet
ween the step input and the grasp output is minimized, with absence of
oscillation as a constraint. The difference between the system's RMS
ramp tracking errors for the two tuning methods was less than 1% of th
e ramp size regardless of the initial values of the parameters, implyi
ng that the tuning methods were equivalent. However, the parallel tuni
ng method was faster and required fewer trials than the sequential met
hod. The capability of the closed-loop system to regulate grasp output
in the presence of disturbances was compared with the capability with
out feedback. Patients were instructed to either grasp an object at a
certain force level or to match a certain grasp opening. They would th
en lock their command at a fixed value, and either remain immobile to
test time dependence or pronate and supinate their forearm to test pos
tural disturbances. With closed-loop control, the grasp output was bet
ter regulated in the presence of disturbances, with an average output
variance 60% lower than without feedback control.