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Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
PLoS ONE | Year: 2015

One of the core features of autism spectrum disorder (ASD) is impaired reciprocal social interaction, especially in processing emotional information. Social robots are used to encourage children with ASD to take the initiative and to interact with the robotic tools to stimulate emotional responses. However, the existing evidence is limited by poor trial designs. The purpose of this study was to provide computational evidence in support of robot-Assisted therapy for children with ASD. We thus propose an emotional model of ASD that adapts a Bayesian model of the uncanny valley effect, which holds that a human-looking robot can provoke repulsion and sensations of eeriness. Based on the unique emotional responses of children with ASD to the robots, we postulate that ASD induces a unique emotional response curve, more like a cliff than a valley. Thus, we performed numerical simulations of robot-Assisted therapy to evaluate its effects. The results showed that, although a stimulus fell into the uncanny valley in the typical condition, it was effective at avoiding the uncanny cliff in the ASD condition. Consequently, individuals with ASD may find it more comfortable, and may modify their emotional response, if the robots look like deformed humans, even if they appear "creepy" to typical individuals. Therefore, we suggest that our model explains the effects of robot-Assisted therapy in children with ASD and that humanlooking robots may have potential advantages for improving social interactions in ASD. Copyright: © 2015 Yuki Ueyama. Source


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

We evaluated the efficiency of robotic therapy for stroke survivors by using a computational approach in motor theory with a stroke rehabilitation model. In computational neuroscience, hand movement can be represented by population coding of neuronal preferred directions (PDs) in the motor cortex. We modeled the recovery processes of arm movement in conventional and robotic therapies as reoptimization of PDs in different learning rules, and compared the efficiencies after stroke. Conventional therapy did not induce complete recovery of stroke lesions, and the neuronal state depended on the training direction. However, robotic therapy reoptimized the PDs uniformly regardless of the training direction. These observations suggest that robotic therapy may be effective for recovery and not have a negative effect on motor performance depending the training direction. Furthermore, this study provides computational evidence to promote robotic therapy for stroke rehabilitation. © 2014 Springer International Publishing. Source


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

We propose a computational model for anti-Bayesian sensory integration of human behavioral actions and perception in the size-weight illusion (SWI). The SWI refers to the fact that people judge the smaller of two equally weighted objects to heavier when lifted. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. However, the SWI cannot be explained on the basis of Bayesian integration, and the nervous system is thought to use two entirely different mechanisms to integrate prior expectations with current sensory information about object weight. Our proposed model is defined as a state estimator, combining a Kalman filter and a H∞ filter. As a result, the model not only predicted the anti-Bayesian estimation of the weight but also the Bayesian estimation of the motor behavior. Therefore, we hypothesize that the SWI is realized by a H∞ filter and a Kalman filter. © Springer International Publishing Switzerland 2014. Source


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Several studies have aimed to provide computational evidence of poststroke interventions because how to optimize motor recovery has been unclear. Although muscle synergies may be the basic control modules on which the central nervous system relies to generate motion, previous computational evidence ignored muscle activity. This study proposes a model of motor impairment after stroke for predicting muscle activity patterns. This model can reproduce a peculiar muscle activation pattern observed in stroke patients. Moreover, we carried out a simulation of the motor recovery process by minimizing the output torque error. As a result, the muscle activation patterns could not be modified to the intact condition, because the recovery process might fall into a local minimum. Thus, we suggest that our model could reproduce muscle activities after stroke, and that muscle synergy cannot be recovered by ‘conventional’ processes of the poststroke rehabilitation. © Springer International Publishing Switzerland 2015. Source


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
International Workshop on Advanced Motion Control, AMC | Year: 2014

We investigated the role of feedback gain in optimal feedback control (OFC) theory using a neuromotor system. Neural studies have shown that directional tuning, known as the 'preferred direction' (PD), is a basic functional property of cell activity in the primary motor cortex (M1). However, it is not clear which directions the M1 codes for, because neural activities can correlate with several directional parameters, such as joint torque and end-point motion. Thus, to examine the computational mechanism in the M1, we modeled the isometric motor task of a musculoskeletal system required to generate the desired joint torque. Then, we computed the optimal feedback gain according to OFC. The feedback gain indicated directional tunings of the joint torque and end-point motion in Cartesian space that were similar to the M1 neuron PDs observed in previous studies. Thus, we suggest that the M1 acts as a feedback gain in OFC. © 2014 IEEE. Source

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