A mirror therapy system using virtual reality and an actuated exoskeleton for the recovery of hand motor impairments: a study of acceptability, usability, and embodiment.

Hand motor impairments are one of the main causes of disabilities worldwide. Rehabilitation procedures like mirror therapy are given crucial importance. In the traditional setup, the patient moves the healthy hand in front of a mirror; the view of the mirrored motion tricks the brain into thinking that the impaired hand is moving as well, stimulating the recovery of the lost hand functionalities. We propose an innovative mirror therapy system that leverages and couples cutting-edge technologies. Virtual reality recreates an immersive and effective mirroring effect; a soft hand exoskeleton accompanies the virtual visual perception by physically inducing the mirrored motion to the real hand. Three working modes of our system have been tested with 21 healthy users. The system is ranked as acceptable by the system usability scale; it does not provoke adverse events or sickness in the users, according to the simulator sickness questionnaire; the three execution modes are also compared w.r.t. the sense of embodiment, evaluated through another customized questionnaire. The achieved results show the potential of our system as a clinical tool and reveal its social and economic impact.

How to compete with robots by assessing job automation risks and resilient alternatives.

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers' automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.