Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model

We discuss the computation of somatosensory information from motion-capture data. The efficient computational algorithms previously developed by the authors for multibody systems, such as humanoid robots, are applied to a musculoskeletal model of the human body. The somatosensory information includes tension, length, and velocity of the muscles, tension of the tendons and ligaments, pressure of the cartilages, and stress of the bones. The inverse dynamics of the musculoskeletal human model is formulated as an optimization problem subject to equality and inequality conditions. We analyzed the solutions obtained by linear and quadratic programming methods, and showed that linear programming has better performance. The technological development aims to define a higher dimensional man-machine interface and to open the door to the cognitive-level communication of humans and machines.