Estimating sensorimotor disorders using Bayesian theory and probabilistic graphical models with mixed reality technology

Assessing sensorimotor problems is challenging due to the level of uncertainty present in the Central Nervous System (CNS). To solve this problem, this work introduces a novel strategy that combines Bayesian Theory for motor control with Probabilistic Graphical Models to estimate sensorimotor disorders. Therefore, by integrating these frameworks, we provide a precise estimation of the presence, or absence, of a sensorimotor problem over time. As first step, the user performs a task in a Mixed Reality environment, which guidesthe user through a series of activitiesto examine their eye-hand coordination under uncertainty. This portion of the system collects sampled information about the user’s sensory output for analysis of the user’s optimal motor control. Thence, after collection of sensory information, the system detects patterns of drastic changes of variability by comparing the prior and posterior distributions of two senses (vision and proprioception). Then using statistical inference, we determine if the user is following a pattern of variability or not. To further support the fact of detecting a pattern of irregular control, we use a Bayesian Network to condition a user’s medical and personal information to infer their expected pattern of motor control variance. Then, we join the evidence from the observed and expected patterns to deduce an observed state. Ultimately, a sequence of observed states is provided to the Hidden Markov Model to estimate a sensorimotor disorder from the provided evidence. With the estimated results, from simulations, we obtained reliable information about the user’s sensorimotor performance overtime so proper decisions could be made to assess a user’s coordination condition. Therefore, the system is capable to estimate expected results regarding sensorimotor disorders when provided with multiple states of evidence.