Genetic algorithm-based adaptive weight decision method for motion estimation framework

Recently, the diverse virtual reality devices are developed and utilized. Particularly, the devices that recognize the motions of users such as griping hands and opening hands are issued to utilize the motions of the users as input methodology. Traditional research about motion recognition suggests user's motion estimation methods by calculating Bayesian probability after measuring the orientation of the motions by a Myo, which is one of contact-type motion recognition devices. However, the motion estimation methods have the problem of low motion estimation accuracy, given that orientation is defined by x, y, and z, which are calculated separately only considering the values of the corresponding axis. In order to improve motion estimation accuracy, motions should be estimated by considering the values of all axis. This paper proposes a method using genetic algorithm to calculate weights, which are applied to estimate motions through Bayesian probability by considering the values of all axis after measuring user's motions with a Myo. The proposed method consists of three steps. First, the Bayesian probability is calculated by considering the correlations of x, y, and z of the orientation of a Myo. Second, weights are determined by applying genetic algorithm. Third, motions are estimated through the Bayesian probability with the determined weights. Experiments were conducted to compare the Bayesian probability between the traditional method based on min/max and the proposed method, which showed that the proposed method had reduced the difference of the orientations by 32%.