Abstract
The human sensibility caused by the motion of an object grasped by a human operator is defined as kinesthetic sense of arm. Due to nonlinearity and ambiguity of human sense, there is no absolute standard for quantification of kinesthetic sense. In this research, a so-called 2-dimensional arm motion generator is developed to present various mechanical impedance (i.e., stiffness or damping) characteristics to a human arm. The kinesthetic words representing arm kinesthetic sense are selected and then the subject's satisfaction levels on these words for given impedance values are measured and processed by the SD method and factor analysis. In addition, the quantification method using neural network is proposed to take into account the individual difference between the mean sensibility and each subject's sensibility. Through this proposed algorithm, the sensibility of human motion described qualitatively can be converted into engineering data ensuring objectivity, reproducibility, and universality.