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

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.

[1]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[2]  J. Challis.  Producing physiologically realistic individual muscle force estimations by imposing constraints when using optimization techniques. , 1997, Medical engineering & physics.

[3]  E. Forster,et al.  Extension of a state-of-the-art optimization criterion to predict co-contraction. , 2004, Journal of biomechanics.

[4]  Yoky Matsuoka,et al.  Biological stiffness control strategies for the Anatomically Correct Testbed (ACT) hand , 2008, 2008 IEEE International Conference on Robotics and Automation.

[5]  David W. Franklin,et al.  Human-humanoid interaction: is a humanoid robot perceived as a human? , 2004, Humanoids.

[6]  Yoshihiko Nakamura,et al.  Mimesis Scheme using a Monocular Vision System on a Humanoid Robot , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  Pierre Alart,et al.  Conjugate gradient type algorithms for frictional multi-contact problems: applications to granular materials , 2005 .

[8]  Katsu Yamane,et al.  Estimation of Physically and Physiologically Valid Somatosensory Information , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  K. Doya,et al.  A unifying computational framework for motor control and social interaction. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[10]  Katsu Yamane,et al.  Markerless Motion Capture with Structure Estimation Capability , 2008, J. Robotics Mechatronics.

[11]  R. Crowninshield,et al.  A physiologically based criterion of muscle force prediction in locomotion. , 1981, Journal of biomechanics.

[12]  C. Sherrington,et al.  Reflexes in Response to Stretch (Myotatic Reflexes) , 1924 .

[13]  Y. Nakamura,et al.  Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model , 2005, IEEE Transactions on Robotics.

[14]  Katsu Yamane,et al.  Capture Database through Symbolization, Recognition and Generation of Motion Patterns , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[15]  M. Damsgaard,et al.  Muscle recruitment by the min/max criterion -- a comparative numerical study. , 2001, Journal of biomechanics.

[16]  David E. Orin,et al.  Kinematic and kinetic analysis of open-chain linkages utilizing Newton-Euler methods , 1979 .

[17]  Gordon Cheng,et al.  Human-humanoid interaction: is a humanoid robot perceived as a human? , 2004, 4th IEEE/RAS International Conference on Humanoid Robots, 2004..