Complementary Limb Motion Estimation ( CLME ) : Experimental Results

Rehabilitation robots facilitate early and extensive therapy, which promotes effective rehabilitation after brain injury [Sinkjaer2005]. Control strategies for these robots can be based on recent studies concerning motor learning and rehabilitation. These have shown that active movements performed by the subject, and better still, combined with error enhancement, are much more effective than guided motion along the “correct” path [Patton2006]. Therefore, the ideal would be to leave as much freedom to the patient as possible or even to disturb him, such that he has to train his voluntary activity. However, the functionality of walking is crucial also during therapy: Without intervention, the patient might get hurt due to inappropriate movements, or even fall. One compromise to solve this dilemma is to provide assistance only when essential for the walking task, and to leave the patient otherwise undisturbed, an approach similar to new tendencies in upper limb rehabilitation [Oldewurtel2007]. Therefore, a controller has to fulfill two requirements: It has to ensure stable walking, and it has to remain “invisible” if the patient walks correctly. The definition of “correct” is frequently resolved by comparing the gait directly to another healthy subject’s gait. However, position control of both legs along the gait pattern of someone else interferes with the subject’s motion intention, both due to differences between subjects, as well as to step-to-step variance in one subject. This interference manifests itself in considerable interaction forces between leg and exoskeleton [Jezernik2004]. Interference can be lowered if the patient’s motion intention is known, such that the robot can anticipate and adapt the reference trajectory. In some cases, the intention can be deduced from insufficient muscular activity in the impaired limbs, either by observation of the generated motion [Kazerooni2005, Jezernik2004], or by EMG measurements of the muscle activation. However, these techniques require sufficiently coordinated activity in the brain regions controlling the impaired limbs. An alternative approach is to observe the patient’s sound limbs, which might reveal the motion intention. We have presented an automated, generic method (”Complementary Limb Motion Estimation”, CLME), which infers from the motion of sound limbs to the intended motion of paretic or amputated limbs [Vallery2006]. The idea is based on the observation that during functional movements such as grasping or walking, there are strong couplings between Degrees of Freedom (DoFs), called ”synergies” [StOnge2003]. This indicates a subset of manipulated variables; it seems as if our brain has developed such refined control methodologies to deal with the abundance of human DoFs. Based on these couplings in healthy synergistic motion, CLME estimates the motion of paretic limbs that corresponds to a patient’s current sound limb motion. CLME thus defines “correct” walking only on the basis of interjoint couplings, such that a much wider range of movements is allowed compared to a predefined gait pattern. Furthermore, sound limbs are not influenced in their motion at all. The strong inter-limb coordination during human walking allows for a very accurate right leg left leg inference (as needed for hemiparetic subjects) using prerecorded trajectories in simulations [Vallery2006]. However, the suitability for control of gait rehabilitation robots can only be answered by practical experiments, where the human closes the loop. The pilot studies to be presented here therefore address the two requirements above: a) In the case of a high level of paresis, is stable walking ensured by the controller? b) In the case of voluntary activity in the impaired leg, will the controller allow functional gait with only minor interference?