Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

Abstract

Abstract-We have developed a haptic-based approach for retraining of interjoint coordination following stroke called time-independent functional training (TIFT) and implemented this mode in the ARMin III robotic exoskeleton. The ARMin III robot was developed by Drs. Robert Riener and Tobias Nef at the Swiss Federal Institute of Technology Zurich (Eidgenossische Technische Hochschule Zurich, or ETH Zurich), in Zurich, Switzerland. In the TIFT mode, the robot maintains arm movements within the proper kinematic trajectory via haptic walls at each joint. These arm movements focus training of interjoint coordination with highly intuitive real-time feedback of performance; arm movements advance within the trajectory only if their movement coordination is correct. In initial testing, 37 nondisabled subjects received a single session of learning of a complex pattern. Subjects were randomized to TIFT or visual demonstration or moved along with the robot as it moved though the pattern (time-dependent [TD] training). We examined visual demonstration to separate the effects of action observation on motor learning from the effects of the two haptic guidance methods. During these training trials, TIFT subjects reduced error and interaction forces between the robot and arm, while TD subject performance did not change. All groups showed significant learning of the trajectory during unassisted recall trials, but we observed no difference in learning between groups, possibly because this learning task is dominated by vision. Further testing in stroke populations is warranted.

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

“…In [95] the time-independent functional training (TIFT) algorithm was introduced: virtual joint-space walls kept the subject close to the ideal joint-space path, acting simultaneously on multiple joints motion as PD position controls. This strategy provided assistance as correction to bad motion coordination of the main joints active during the task, in particular on shoulder and elbow joints.…”

Using the kinect to limit abnormal kinematics and compensation strategies during therapy with end effector robots

“…The TIFT mode limits progression through a trajectory based on the lagging joint. The method is described in detail in Brokaw et al 2011 [24]. The general principle of the methodology is the use of haptic walls to limit movement unless the user moves both control joints together.…”

“…From the current position on the trajectory the user needs to enter the yellow coordination space to continue progressing [24]. This yellow goal space will move as the current trajectory position changes.…”

The road ahead for rehabilitation robotics

“…Three articles tackle the critical issue of determining the most appropriate control algorithms and human-machine interfaces. Brokaw et al describe a novel joint-based algorithm for training functional activities with the ARMin robot [7], and Acosta et al show that while video games can provide a motivational interface, they will be most effective if designed to target specific impairments [8]. While adaptive control algorithms are under development based on actual task performance, Koenig et al demonstrate the feasibility of real-time estimation of psychological state (i.e., motivation, stress level, attention), which can be used to optimally grade task difficulty [9].…”