Basketball free-throw training with augmented reality-based optimal shot trajectory for novice shooters.

We propose an augmented reality (AR)-based training system for basketball free-throws. The optimal shot trajectory for free-throws is projected by a head-mounted display according to the shooter's release point. The efficacy of the training system was assessed in novice shooters by comparing changes in success rates and eye-gaze behavior (quiet eye [QE]) between AR-training and control-training groups.

Costs of position, velocity, and force requirements in optimal control induce triphasic muscle activation during reaching movement.

The nervous system activates a pair of agonist and antagonist muscles to determine the muscle activation pattern for a desired movement. Although there is a problem with redundancy, it is solved immediately, and movements are generated with characteristic muscle activation patterns in which antagonistic muscle pairs show alternate bursts with a triphasic shape. To investigate the requirements for deriving this pattern, this study simulated arm movement numerically by adopting a musculoskeletal arm model and an optimal control.

Optimal feedback control to describe multiple representations of primary motor cortex neurons.

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control.

A Bayesian Model of the Uncanny Valley Effect for Explaining the Effects of Therapeutic Robots in Autism Spectrum Disorder.

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.

Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty.

We propose a mini-max feedback control (MMFC) model as a robust approach to human motor control under conditions of uncertain dynamics, such as structural uncertainty. The MMFC model is an expansion of the optimal feedback control (OFC) model. According to this scheme, motor commands are generated to minimize the maximal cost, based on an assumption of worst-case uncertainty, characterized by familiarity with novel dynamics.