Haptic recognition of dystonia and spasticity in simulated multi-joint hypertonia.

This paper investigates the capability of naïve individuals to recognize dystonic- or spastic- like conditions through physical manipulation of a virtual arm. Subjects physically interact with a two-joint, six-muscle hypertonic arm model, rendered on a two degrees-of-freedom robotic manipulandum. This paradigm aims to identify the limitation of manual manipulation during diagnosis of hypertonia. Our results indicate that there are difficulties to discriminate between the two conditions at low to medium level of severity. We found that the sample entropy of the executed motion and the force experienced during physical manipulation, tended to be higher during incorrectly identified trials than in those correctly assessed.