Human-in-the-Loop Optimization of Active Back-Support Exoskeleton Assistance Via Lumbosacral Joint Torque Estimation

The assistive profile of an active back support exoskeleton is strongly dependent on the manual tuning of controller gains based on previous experience and trial-and-error. Human-in-the-loop (HIL) optimization allows for automatic tuning of assistive profiles to different subjects. Most HIL methods make use of intrusive sensors that could affect out-of-the-lab exoskeleton adoption. Therefore, we propose a HIL-based assistive controller architecture using only one single IMU that can be easily embedded in any exoskeleton system. To validate our algorithm we recruited 3 subjects and asked them to perform a series of successive load liftings. Meanwhile, we analysed the back-muscles activations focusing on cumulative activation (iEMG), and median activation. We also monitored the total torque generated by the exoskeleton. With respect to an assistance-less condition, the proposed controller resulted in up to 19% reduction of the back-muscles activity. Moreover, compared to a state-of-the-art controller that produced up to 15% reduction of the back-muscles activity, the new controller also required generation of 4% less exoskeleton torque.