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.
Optimal Posture and Supporting Hand Force Prediction for Common Automotive Assembly One-Handed Tasks
Contributed by the Mechanisms and Robotics Committee of ASME for publication in the JOURNAL OF MECHANISMS AND ROBOTICS. Manuscript received March 17, 2012; final manuscript received September 11, 2013; published online March 12, 2014. Assoc. Editor: Kazem Kazerounian.
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Howard, B., Yang, J., and Ozsoy, B. (March 12, 2014). "Optimal Posture and Supporting Hand Force Prediction for Common Automotive Assembly One-Handed Tasks." ASME. J. Mechanisms Robotics. May 2014; 6(2): 021009. https://doi.org/10.1115/1.4025749
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