Quantification of arm kinesthetic sense using an arm motion generator

The human response caused by the motion of an object grasped by a human operator is defined as an arm kinesthetic sense. Due to nonlinearity and ambiguity of human senses, there is no absolute standard for quantification of kinesthetic sense. In this research, a so-called two-dimensional (2-D) arm motion generator is developed to emulate various mechanical impedance, i.e., stiffness or damping, characteristics of a human arm. The 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 semantic differential method and factor analysis. In addition, in order to reflect the individual differences of each subject in the arm kinesthetic sense, compensation for individual differences based on the neural network technique is proposed. Through this proposed algorithm, the human sensations to arm movements described qualitatively can be converted into engineering data ensuring objectivity, reproducibility, and universality. This database can be used to develop user-friendly products related to arm motion.