Muscle tension database for contact-free estimation of human somatosensory information

Abstract

Contact-free estimation of the human somatosensory information is an essential skill for robots working in daily environments. The main objective of this paper is to develop a method for estimating muscle tensions without any sensors attached to the body. Muscle tension is an important information for evaluating physical load during motions. Existing approaches utilizing optimization techniques and/or electromyography (EMG) signals are not appropriate due to lack of physiological validity or usage of electrodes. In this paper, we propose to use a database of muscle tension distribution for obtaining physiologically realistic muscle tensions only from motion data. Using such database instead of direct EMG measurement is justified by the fact that muscle tension distribution is relatively highly correlated even among different subjects. For each new motion frame, we search for a similar entry in the database and use the corresponding muscle tension distribution to estimate the current muscle tensions. We demonstrate that the muscle tensions obtained by this approach is much closer to the result using the EMG data than that using pure numerical optimization, even when the database is constructed from other person's data.