Quite often people are faced with one handed tasks in which the other hand is needed for support. Without these supporting external forces, postures may be unstable, rendering the task impossible. Automotive assembly line operators are confronted with these types of tasks every day, such as hose installations and the connection of electrical components. Determining the optimal location and forces for the supporting hand is important to minimize potential injuries of operators. Traditionally, these supporting hand forces are measured by experiments. This work attempts to provide an important predictive tool that promises to be of considerable value to companies in predicting leaning forces in work simulation for the proactive ergonomic assessment of work tasks. It presents a method using optimization and stability analysis techniques. Stability is based on the calculation of a three dimensional zero moment point (3D-ZMP) and the resultant reaction loads, calculated from the joint torque. The formulation of the optimization problem used to predict the supporting hand forces is presented and tested using tasks commonly encountered by automotive assembly workers. The results are compared to that in literature, providing an initial validation of the methods. The predicted external forces fell within the 95% confidence intervals calculated from the literature for all tasks.
An understanding of human seated posture is important across many fields of scientific research. Certain demographics, such as pregnant women, have special postural limitations that need to be considered. Physics-based posture prediction is a tool in which seated postures can be quickly and thoroughly analyzed, as long the predicted postures are realistic. This paper proposes and validates an optimization formulation to predict seated posture for pregnant women considering ground and seat pan contacts. For the optimization formulation, the design variables are joint angles (posture); the cost function is dependent on joint torques. Constraints include joint limits, joint torque limits, the distances from the end-effectors to target points, and self-collision avoidance constraints. Three different joint torque cost functions have been investigated to account for the special postural characteristics of pregnant women and consider the support reaction forces (SRFs) associated with seated posture. Postures are predicted for three different reaching tasks in common reaching directions using each of the objective function formulations. The predicted postures are validated against experimental postures obtained using motion capture. A linear regression analysis was used to evaluate the validity of the predicted postures and was the criteria for comparison between the different objective functions. A 56 degree of freedom model was used for the posture prediction. Use of the objective function minimizing the maximum normalized joint torque provided an R² value of 0.828, proving superior to either of two alternative functions.
People often complete tasks using one hand for the task and one hand for support. These one-handed support tasks can be found in many different types of jobs, such as automotive assembly jobs. Optimization-based posture prediction has proven to be a valid tool in predicting the postures necessary to complete the tasks, but the related external support forces have been prescribed and not predicted. This paper presents a method in which the optimal posture and related supporting hand forces can be predicted simultaneously using optimization and stability analysis techniques. Postures are evaluated using a physics-based human performance measure (HPM) while external forces are assessed using stability analysis. The physics-based performance measures are based on joint torque. Stability is analyzed using criteria based on a 3D zero moment point (ZMP). The human model used in the prediction contains 56 degrees of freedom and is based on a 50th percentile female in stature. Tasks based on common automotive assembly one-handed tasks found in literature are considered as examples to test the proposed method. Overall, the predicted supporting hand forces have good correlation with experimentally measured forces.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.