Energy-based Adaptive Control and Learning for Patient-Aware Rehabilitation

In this paper we propose a novel energy-based control scheme for an assist-as-needed 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 his/her 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 Simscape® 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 torque-controlled robots, and does not require the use of EMG sensors or precise force measurements.