在经验数字人体模型中使用混合线性效应模型的改进建模方法

Martin Fleischer
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引用次数: 1

摘要

在设计自动驾驶汽车内部时,需要考虑与驾驶无关的任务和接管机动。这些接管是关键时刻,因为驾驶员需要尽快收回对车辆的控制。为了实现这一点,室内设计师需要设计出足够的空间来进行这种运动。本文提出了一种修正的建模方法,使用混合线性效应模型来预测接管场景中手的抓取运动。一项有52名参与者进行抓握运动的研究对通过动作捕捉获得的数据进行了建模。参与者被指示从安装在他们面前的预定义抓取元素中进行运动。使用基于标记的动作捕捉系统记录了手的轨迹。可以观察到,轨迹可以假定为二维现象,因为它们似乎位于一个平面上。因此,轨迹被建模为1+2维问题。平面的一维模型和轨迹的二维模型。本文所描述的抓取轨迹模型采用四次多项式建模。在旧的方法中,对多项式的每个常数用四种不同的模型来建模轨迹。本文采用一种新的建模方法将多项式合并为一个模型。这大大增加了R²m和R²c,并导致了对人类抓取运动本质的三个重大发现:任务因素,如抓取手柄和手柄位置,在抓取轨迹中起主要作用。身体高度在手部运动轨迹建模中起着重要作用。性别、年龄和惯用手对轨迹的影响微不足道。本研究中未评估的其他个人人为因素似乎对手部运动没有严重影响。
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Improved modeling approach for the usage of mixed linear effects models in empirical digital human models
When designing the interior of automated cars, it is necessary to take the non-driving related tasks and the take-over maneuver into account. These take-overs are critical moments since the driver needs to take back control of the vehicle as fast as possible. To facilitate this, interior designers need to design the cabin with enough space to carry out this movement. This paper presents a revised modelling approach using mixed linear effects models to predict the grasping movement of the hand during take-over scenarios. A study with 52 participants doing grasping movements was carried out to model the data obtained via motion capture. The participants were instructed to carry out movements from predefined grasping elements mounted in front of them. The trajectory of the hand was recorded using a marker-based motion capturing system. It is observed that the trajectories can be assumed as a two-dimensional phenomenon, since they seem to lie on one plane. Thus, the trajectories were modeled as a 1+2-dimensional problem. A one-dimensional model for the plane and a second two-dimensional model for the trajectory. The model of grasping trajectory described in this paper was modeled using 4 th degree polynomials. In older approaches, the trajectory was modeled in four different models for each constant of the polynomial. In this paper a new modeling approach is used to merge the polynomial into one model. This increased the R² m and R² c drastically and led to three major discoveries on the nature of human grasping movements: Task factors, such as grasping handle and handle position, play the major role in the grasping trajectory. Body height plays a role in the modelling of hand trajectories. Gender, age, and dominant hand show only negligible influence on the trajectory. Other individual human factors not evaluated in this study do not seem to heavily influence the hand movement.
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