Determining human upper limb postures with an improved inverse kinematic method

Abstract

Body posture predicting methods have many applications, including product design, ergonomic workplace design, human body simulation, virtual reality and animation industry. Initiated in robotics, Inverse Kinematic (IK) method has been widely applied to proactive human body posture estimation. The Analytic Inverse Kinematic (AIK) method is a convenient and time-saving type of IK methods. It is also indicated that, based on AIK methods, a specific body posture can be determined by the optimization of an arbitrary objective function. The objective of this thesis is to predict the postures of human arms during reaching tasks. In this research, a human body model is established in MATLAB, where the Middle Rotation Axis (MRA) analytic kinematic method is accomplished, based on this model. The joint displacement function and joint discomfort function are selected to be initially applied in this improved AIK method. Then a bi-criterion objective function is proposed by integrating the joint displacement function and joint discomfort function, with the suboptimal value of the coefficient, in the integrated objective function, determined by golden section search. Results show that neither the joint displacement function nor the joint discomfort function predicts postures that are close enough to natural upper limb postures of human being, during reaching tasks. The accuracy of the arm postures, predicted by the proposed objective function, is the most satisfactory.