Preference-based assistance optimization for lifting and lowering with a soft back exosuit

Wearable robotic devices have become increasingly prevalent in both
occupational and rehabilitative settings, yet their widespread adoption
remains inhibited by usability barriers related to comfort, restriction,
and noticeable functional benefits. Acknowledging the importance of user
perception in this context, this study explores preference-based
controller optimization for a back exosuit that assists lifting.
Considering the high mental and metabolic effort discrete motor tasks
impose, we used a forced-choice Bayesian Optimization approach that
promotes sampling efficiency by leveraging domain knowledge about just
noticeable differences between assistance settings. Optimizing over two
control parameters, preferred settings were consistent within and uniquely
different between participants. We discovered that overall, participants
preferred asymmetric parameter configurations with more lifting than
lowering assistance, and that preferences were sensitive to user
anthropometrics. These findings highlight the potential of perceptually
guided assistance optimization for wearable robotic devices, marking a
step towards more pervasive adoption of these systems in the real-world.