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首页> 外文会议>Virtual Conference on Human Aspects of Transportation;International Conference on Applied Human Factors and Ergonomics >Development of Statistical Models for Predicting Automobile Seat Fit of Drivers
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Development of Statistical Models for Predicting Automobile Seat Fit of Drivers

机译：推动驾驶员汽车座椅统计模型的统计模型

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摘要

The present study is intended to develop statistical models for predicting automobile seat fit based on the relationships between seat dimensions and subjective seat fit. The evaluations of the subjective seat fit for 43 different driver seats and the seat dimensions at six cross-sectional planes (three for the seatback and the other three for the cushion) were measured and evaluated by eight seat-engineers. The best subset logistic regression analyses were conducted to quantify the relationships between the measured seat dimensions and evaluated subjective seat fit at each of the cross-sectional planes. As a result, significant seat dimensions, such as insert width or bolster height, on the subjective seat fit were identified. The developed logistic models show 90% overall classification accuracy at each section with 80% accuracy with five-fold cross-validation. The developed models would be particularly useful to support seat engineers by providing recommended seat dimensions, which could increase seat fit. In addition, the model is useful to reduce development costs for an automobile seat and increase work efficiency in the digital evaluation process of an automobile seat.
机译：本研究旨在基于座椅尺寸和主体座椅贴合之间的关系来开发用于预测汽车座椅配合的统计模型。通过八个座椅工程师测量并评估主体座椅的主体座椅和六个不同的驾驶员座椅和六个横截面上的座椅尺寸（用于座椅靠背的三个和其他三个的座椅尺寸）。进行了最佳的子集逻辑回归分析以量化测量的座椅尺寸与在每个横截面上的主体座椅配合的关系。结果，鉴定了主体座椅配合上的显着座椅尺寸，例如插入宽度或垫底高度。开发的逻辑模型在每个部分显示90％的整体分类精度，精度为80％，交叉验证五倍。开发的模型通过提供推荐的座椅尺寸来支持座椅工程师，这可能是可以提高座椅合适的。此外，该模型可用于降低汽车座椅的开发成本，并提高汽车座椅的数字评估过程中的工作效率。

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