Opening a door, turning a steering wheel, rotating a coffee mill are typical examples of human movements constrained by the external environment. The constraints decrease the mobility of the human arm and leads to the redundancy in the distribution of the interaction force between the arm joints. Due to the redundancy of the force actuation in the constrained motions, there is infinite number of ways to form the trajectory of the arm. However, human forms the hand trajectory in a unique way. How does human resolve the redundancy of the constrained motions and specify the hand trajectory? To investigate these problems, we examine the trajectory of human arm in a crank rotation task. To explain the trajectory formation in constrained point-to-point motions, we formulate an optimal control problem and propose a novel criterion minimizing the hand contact force change and muscle force change over the time of movement. The simulation results are compared with human motion and force profiles obtained experimentally. It is shown that the novel criterion captures the characteristics of the human constrained motion much more satisfactory than conventional criteria accepted in the research community.
[Show abstract][Hide abstract] ABSTRACT: Opening a door, turning a steering wheel, rotating a coffee mill are typical examples of human movements that require physical interaction with external environment. In these tasks, the human arm is kinematically constrained by the external environment. Although there are infinite possibilities for human subject to select his/her arm trajectories as well as interacting forces, experimental data of human constrained motion show that there exists some regulation inherent in all the measurement data. It is suggested in this paper that in the constrained movements human optimizes the criterion that minimizes the change of the hand contact forces as well as the muscle forces. This criterion differs from the minimum torque change criterion, predicting unconstrained reaching movements. Our experiments show close matching between the prediction and the subjects' data. Therefore, human may use different optimization strategies when performing constrained movements.
[Show abstract][Hide abstract] ABSTRACT: There are many tasks that requires us to interact with physical environment, such as opening a door, turning a steering wheel, rotating a coffee mill, et al.. In these tasks, the arm is usually constrained to the environmental geometry. Although there are infinite possibilities for human subject to select his/her arm trajectories as well as interacting forces when performing the tasks, experiments of human constrained motion however show that there clearly exist some characteristics inherent in all measurement data. Specifically, in this research, it is shown that, when human rotating a crank, he/she optimizes the criterion that minimizes the change of both the end-effector force as well as the muscle forces. This numerical result is strongly supported by human experiments data. Since this criterion is different from the minimum torque change criterion proposed to evaluate human reaching movement in free motion space, it is then suggested that human may use different optimal strategies with respect to different task requirements as well as environmental conditions
Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on; 09/2004
[Show abstract][Hide abstract] ABSTRACT: This paper presents studies of the coordination of human upper body voluntary movement. A minimum-jerk 3D model is used to obtain the desired path in Cartesian space, which is widely used in the prediction of human reach movement. Instead of inverse kinematics, a direct optimization approach is used to predict each joint's profile (a spline curve). This optimization problem has four cost function terms: (1) Joint
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 target point to the final target point; (3) The non-smoothness
function of the trajectory, which is the second derivative of the joint trajectory; (4) The non-continuity
function, which consists of the amplitudes of joint angle rates at the initial and final target points, in order to emphasize smooth starting and ending conditions. This direct optimization technique can be used for potentially any number of degrees of freedom (DOF) system and it reduces the cost associated with certain inverse kinematics approaches for resolving joint profiles. This paper presents a high redundant upper-body modeling with 15 DOFs. Illustrative examples are presented and an interface is set up to visualize the results.
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