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A Comfort Analysis Model based on SVM Computation

机译:基于支持向量机计算的舒适度分析模型

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摘要

The emulation of the ergonomics design on automobile requires us to combine human models with prediction of body comfort. The comfort prediction model of posture needs to express the real driving instances. Most of the ergonomics systems used posture databases and reverse-movement kinematics in order to adjust a posture. However, it is difficult to describe the body comfort involving human body joints. This paper introduces a research on comfort prediction model, which is based on our experiments from which we have obtained the relations among driving posture, and the body parameters. Using Support Vector Machine (SVM) regression analysis methods, we have derived the functional expression between these parameters, which formed the basis of a prediction model on driving posture. In this approach, a sport-chain model was used to confirm the driving posture, which was captured using motion-capture equipment, with the marking points pasted on the surface of a human body.
机译:汽车上人机工程学设计的仿真要求我们将人体模型与预测人体舒适性相结合。姿势的舒适度预测模型需要表达真实的驾驶实例。大多数人体工程学系统使用姿势数据库和反向运动运动学来调整姿势。但是,很难描述涉及人体关节的身体舒适度。本文介绍了舒适性预测模型的研究,该模型是基于我们的实验而得出的,该模型获得了驾驶姿势与身体参数之间的关系。使用支持向量机(SVM)回归分析方法,我们得出了这些参数之间的函数表达式,从而为驾驶姿势预测模型奠定了基础。在这种方法中,运动链模型用于确认驾驶姿势,该姿势是使用运动捕捉设备捕获的,并将标记点粘贴在人体表面上。

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