Motor adaptation and internal model formation in a robot-mediated forcefield

Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcranial magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical excitability.Objective:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield.Methods:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation task.There were two conditions:unperturbed non-adaptation and perturbed adapta-tion.TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning index.Results:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory errors.Baseline TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor adap-tation.Conclusions:ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation.Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex.TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.

Attention shifting during child-robot interaction:a preliminary clinical study for children with autism spectrum disorder

There is an increasing need to introduce socially interactive robots as a means of assistance in autism spectrum disorder(ASD) treatment and rehabilitation, to improve the effectiveness of rehabilitation training and the diversification of treatment, and to alleviate the shortage of medical personnel in China's Mainland and other places in the world. In this preliminary clinical study, three different socially interactive robots with different appearances and functionalities were tested in therapy-like settings in four different rehabilitation facilities/institutions in Shenzhen, China. Seventy-four participants, including 52 children with ASD, whose processes of interacting with robots were recorded by three different cameras, all received a single-session three-robot intervention. Data were collected from not only the videos recorded, but also the questionnaires filled mostly by parents of the participants. Some insights from the preliminary results were obtained. These can contribute to the research on physical robo it design and evaluations on robots in therapy-like settings. First, when doing physical robot design, some preferential focus should be on aspects of appearances and functionalities. Second, attention analysis using algorithms such as estimation of the directions of gaze and head posture of a child in the video clips can be adopted to quantitatively measure the prosocial behaviors and actions(e.g., attention shifting from one particular robot to other robots) of the children. Third, observing and calculating the frequency of the time children spend on exploring/playing with the robots in the video clips can be adopted to qualitatively analyze such behaviors and actions. Limitations of the present study are also presented.