Joint discomfort human performance measure for driver posture prediction: some insights
by Jared Gragg; Brad Howard; Aimee Cloutier; James Yang
International Journal of Human Factors Modelling and Simulation (IJHFMS), Vol. 3, No. 3/4, 2012

Abstract: Digital human models have proven to be valuable tools for understanding human reach envelope inside a vehicle. Typical digital human posture prediction simulations employ optimisation techniques that find the most likely posture that a human would realise to achieve a given task. Human performance measures are included as an objective function in the optimisation formulation. A previous study (Yang et al., 2004) defined a joint discomfort human performance measure based on joint angles that incorporates three distinct aspects of joint discomfort. However, the previously defined joint discomfort function is poorly understood. This paper investigates the properties of the joint discomfort function and how each parameter in the function affects the predicted posture. An alternate formulation of the joint discomfort human performance measure is proposed. The parameters of the new joint discomfort function are investigated through graphical analysis, ANOVA analysis, and sensitivity analysis. The joint discomfort function is then employed in several posture prediction simulations that pertain to reach space inside a vehicle. The postures are given to demonstrate the effect that the parameters of joint discomfort have on predicted postures.

Online publication date: Fri, 29-Aug-2014

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