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Vertical Ground Reaction Forces for Given Human Standing Posture With Uneven Terrains: Prediction and Validation

机译:给定人类不平坦地形的垂直地面反作用力:预测和验证

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摘要

Ground reaction forces (GRFs) on individual support vary with posture and motion for bipedal mechanisms or systems due to the redundancy in the system. In digital human modeling, specifically posture prediction, the GRFs are predicted, as they are unknown in a virtual environment. Traditionally, models in which the GRFs are predicted have been presented; however, they are always assumed to be on flat ground. Little work has been done to predict the GRFs on uneven or arbitrary terrain. This paper presents a generic method to calculate the vertical GRFs for given standing postures with uneven terrain. The vertical GRFs are predicted based on the generalized forces (torque in revolute joints; force in prismatic joints) calculated using the recursive Lagrangian formulation and a 3-D zero moment point. Motion capture experiments were used to obtain postures for common standing reaching tasks. Force plates were employed to record GRF information for each task. Experimental postures were reconstructed, and the GRF prediction algorithm was used to predict the associated vertical GRFs for each task. Experimental and predicted vertical GRFs are compared to validate the prediction model. The prediction method proved to be valid, with an overall error of 6%.
机译:对于双足机构或系统,由于系统中的冗余性,各个支撑上的地面反作用力(GRF)随姿势和运动而变化。在数字人体建模中,特别是在姿势预测中,可以预测GRF,因为它们在虚拟环境中是未知的。传统上,已经提出了预测GRF的模型。但是,始终假定它们位于平坦的地面上。在不平坦或任意地形上预测GRF的工作很少。本文提出了一种通用方法,用于计算在不平坦地形下给定站立姿势的垂直GRF。垂直GRF是根据使用递归拉格朗日公式和3-D零力矩点计算的广义力(旋转关节的扭矩;棱柱形关节的力)预测的。运动捕捉实验用于获得常见站立站立任务的姿势。使用测力板记录每项任务的GRF信息。重建实验姿势,并使用GRF预测算法预测每个任务的相关垂直GRF。比较实验和预测的垂直GRF,以验证预测模型。该预测方法被证明是有效的,总误差为6%。

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