Optimal Posture and Supporting Hand Force Prediction for Common Automotive Assembly One Handed Tasks

Abstract

People often complete tasks using one hand for the task and one hand for support. These onehanded support tasks can be found in many different types of jobs, such as automotive assembly jobs. Optimizationbased 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 physicsbased human performance measure (HPM) while external forces are assessed using stability analysis. The physicsbased 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 onehanded 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.