Many tasks require the arm to move from its initial position to a specified target position without any constraints or via a point for a curved path in case of obstacle avoidance. In this paper we present a formulation for predicting the human upper body motion. Having obtained the desired path in Cartesian space using the minimum jerk theory and represented each joint motion by a B-spline curve with unknown parameters (i.e., control points), an optimization approach, instead of inverse kinematics, is used to calculate control points of each joint spline curve. Cost function includes multi-part: (1) discomfort function that evaluates displacement of each joint away from its neutral position; (2) inconsistency function, which is the joint rate change (first derivative) and predicted overall trend from the initial point to the end point; (3) nonsmoothness function of the trajectory, which is the second derivative of the joint trajectory; (4) noncontinuity function, which is the amplitudes of joint angle rates at the start and end points. While this work has been limited to a 15-degree-of-freedom (DOF) of the upper body, the theory presented herein is expandable to any part of the body that can be represented as segmental links of a kinematic chain. Illustrative examples are presented and an interface is set up to visualize the results.