Motion quaternion-based motion estimation method of MYO using K-means algorithm and Bayesian probability

There are diverse types of devices based on natural user interface/experience for humanized computing. One such device, the MYO allows the measurement of arm motions and uses them as an interface based on gestures. There are several research works for measuring the arm motions using MYOs. For example, one of the studies defines two types of motions for a forearm and for an upper arm, respectively. The orientations of the two types are measured by two MYOs. Bayesian probabilities are calculated based on the measured orientations and are utilized to estimate the orientations of the upper arm that is not being measured. However, because the orientation of the MYO can be expressed by one quaternion, the Bayesian probability by quaternions is more accurate than the Bayesian probability by each element of quaternions. This paper proposes a motion estimation method to increase the accuracy of motion estimation. The orientations obtained from MYO are expressed by one quaternion and are clustered by K-means. In the experiments, the performance of the proposed method was validated by analyzing the difference between estimated motion quaternions and measured motion quaternions, which showed enhanced performance.