Energybased Adaptive Control and Learning for PatientAware Rehabilitation In this paper we propose a novel energybased control scheme for an assistasneeded rehabilitation strategy, which both adapts the level of support based on patient participation and allows the patient to deviate from the prescribed motion in favor of hisher safety. We build an energy network model, with which we can monitor the energy flow through the system and prescribe a threshold on stored energy. We also develop an adaptive motion control law that shapes the desired trajectory in order to respect the stored energy threshold. Next, we show how adapting the stored energy threshold can be used to change the level of responsiveness to the patient as well as to prevent excessive energy transfer to the human by the system. A criterion is defined for setting this energy threshold, which can be further used for monitoring the patient active participation and for adapting and learning the appropriate assistance level during rehabilitation. Experimental results based on implementation in MATLAB Simscapexc2xae and on the VEMO robotic system demonstrate the feasibility of the suggested approach. The presented control scheme can be applied to any system, including position and torquecontrolled robots, and does not require the use of EMG sensors or precise force measurements.

Adaptive Assistasneeded Control Based on ActorCritic Reinforcement Learning In robotassisted rehabilitation, assistasneeded (AAN) controllers have been proposed to promote subjectsxe2x80x99 active participation, which is thought to lead to better training outcomes. Most of these AAN controllers require a patientspecific manual tuning of the parameters defining the underlying forcefield, which typically results in a tedious and timeconsuming process. In this paper, we propose a reinforcementlearningbased impedance controller that actively reshapes the stiffness of the forcefield to the subjectxe2x80x99s performance, while providing assistance only when needed. This adaptability is made possible by correlating the subjectxe2x80x99s most recent performance to the ultimate control objective in realtime. In addition, the proposed controller is built upon action dependent heuristic dynamic programming using the actorcritic structure, and therefore does not require prior knowledge of the system model. The controller is experimentally validated with healthy subjects through a simulated ankle mobilization training session using a powered anklefoot orthosis.