Embodied knowledge extraction from human motion using singular value decomposition

Embodied knowledge is the knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting embodied knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the embodied knowledge extraction using singular value decomposition proposed in this paper.