No abstract

In many robot-assisted rehabilitation and motor skill learning applications, robots generate forces that facilitate movement performance. While there is some evidence that assistance is beneficial, the underlying mechanisms of action are largely unknown, and it is unclear what force patterns are more effective. Here, we investigate how reaching movements (and their neural correlates) are altered by 'assistive' forces. Subjects performed center-out reaching movements, under the influence of a robot-generated force, constant in magnitude and always directed toward the target. The experimental protocol included three phases: (1) baseline (no forces), (2) force field (with two different force levels, 3 N and 6 N, applied in random order), and (3) after-effect (no forces). EEG activity was recorded from motor and frontal cortical areas. In both movement kinematics and EEG activity, we looked at the effects of forces, of adaptation to such forces and at the aftereffects of such adaptation. Assistive forces initially induced a degraded performance and in general alterations in movement kinematics. However, subjects quickly adapted to the perturbation by improving their performance. With regard to EEG activity, we found (1) an increased beta band synchronization just before movements and an alpha band synchronization in the ipsilateral hemisphere, both proportional to force magnitude; (2) a gradual decrease in alpha band synchronization with practice in the contralateral hemisphere; (3) an increase in theta band synchronization in the later stage of the force epochs; and (4) an ipsilateral to contralateral shift (from baseline to aftereffect) of theta band synchronization. These results point to the need for a careful design of assistive forces to effectively facilitate motor performance and motor learning. Moreover, EEG signals exhibit distinct features related to force and adaptation. Therefore, at least in principle, the latter might be used to monitor the learning process and/or to regulate the amount of assistance.

Efficient rowing requires both physical and technical abilities of the human. Teaching and learning of the technical abilities is thereby mainly restricted to on-water training. The aim of this project was to develop a rowing simulator. This simulator should serve as a high-level indoor training tool that can be used by rowing novices and professionals. The users should perceive acoustic, visual, and haptic cues about their current performance and their environment in real time.The newly-developed rowing simulator consists of a rowing boat hull equipped with multiple position sensors attached to the oar and seat. The boat hull was mounted on a podium placed inside a Cave setup. The Cave comprises projection screens, a loudspeaker system, and actuated winches for visual, acoustic, and haptic feedback, respectively. A mathematical real-time rowing model was developed, which computes the boat velocity and the oar force as a function of the movement of the oar and the user. All relevant boat, oar, and user parameters can be arbitrarily set, thus allowing the simulation of different boat types.The rowing model was validated by comparing results of the simulation with data from on-water measurements. Both the boat velocity and the oar force predicted by the model correlated highly with the experimentally-obtained data. Furthermore, the rowing simulator was successfully tested with professional rowers who rated the level of realism and the applicability of the simulator for indoor training as high. Based on the feedback of the rowers, various hardware and software extensions are planned for the simulator, including an increase of the number of actuated degrees of freedom of the boat and the oar, in order to improve the haptic feedback. r

Learning to move skillfully requires that the motor system adjusts motor commands based on ongoing performance, until the task is executed satisfactorily. Robots can be used to emulate motor tasks that involve haptic interaction with objects. These studies may provide useful insights on how humans acquire a novel motor skill. Here we address motor skill learning in a 2D ball putting task, by looking at both kinematic and EEG correlates of learning and performance. Participants grasped the handle of a manipulandum and had to hit a virtual ball in order to put it into a target region (hole). The robot was used to render the contact force with the ball during impact. At every trial, with respect to the initial ball position, the hole appeared in one of three different directions and two distances, selected randomly. The experimental protocol included a total of 300 movements. In movement kinematics we looked at the effects of learning and target distance. In EEG signals, we looked at the effect of learning and the effect of success/failure on the ongoing brain activity. Subjects managed to improve their performance through practice, in all directions and at both target distances. Direction did not affect the performance much, but greater target distance induced greater errors. With regards to the EEG activity, we found that (i) practice led to an increased theta synchronization in the frontal areas; (ii) successful trials were preceded by higher theta synchronization, and alpha and beta desynchronization. These results suggest that EEG signals can be used to monitor the learning process and to predict the outcome (success/failure) of individual trials. These findings open possibilities to develop new schemes to promote and facilitate learning, which integrate EEG and robots.