Shohei Taniguchi

Inverse Dynamics of Human Passive Motion Based on Iterative Learning Control

Estimation of joint torque is an important objective in the analyses of human motion. In particular, many applications seek to discern torque during a desired human motion, which is equivalent to solving the inverse dynamics. The computed torque method is a conventional means of calculating inverse dynamics. The obtained torque, however, invariably includes errors resulting from inexact inertial and viscoelastic parameters. This paper presents a method for solving the inverse dynamics of a human arm passively during tracking. To achieve precise human motion tracking, iterative learning control is used for motion generation. Some experiments that target a human arm are executed to validate the proposed method.

Joint Torque Analysis for Humans in Underwater Environment with Parallel Wire Driven Systems

We propose a new method to estimate joint torque of human arms in underwater environment. In the proposed method, a parallel wire driven system drives an end point of a subjects arm to measure the end-point force, and joint torque is estimated with the measured end-point force and the subjects limb length. Since the force sensors with wire used in this system is small enough, the measured torque can be gained under less influence of hydrodynamics on the sensors. We verify the accuracy of the estimation method experimentally and confirm that the proposed method can estimate joint torque of a human arm. We compare the joint torque data of the same trajectory in the different environment and extract the effect of hydrodynamics on human motions in underwater environment.