  This paper compares three controllers for quasi-passive exoskeletons. The
Utility Maximizing Controller (UMC) uses intent estimation to recognize user
motions and decision theory to activate the support mechanism. The intent
estimation algorithm requires demonstrations for each motion to be recognized.
Depending on what motion is recognized, different control signals are sent to
the exoskeleton. The Extended UMC (E-UMC) adds a calibration step and a
velocity module to trigger the UMC. As a benchmark, and to compare the behavior
of the controllers irrespective of the hardware, a Passive Exoskeleton
Controller (PEC) is developed as well. The controllers were implemented on a
hip exoskeleton and evaluated in a user study consisting of two phases. First,
demonstrations of three motions were recorded: squat, stoop left and stoop
right. Afterwards, the controllers were evaluated. The E-UMC combines benefits
from the UMC and the PEC, confirming the need for the two extensions. The E-UMC
discriminates between the three motions and does not generate false positives
for previously unseen motions such as stair walking. The proposed methods can
also be applied to support other motions.
